@article {1682081, title = {Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing}, year = {Forthcoming}, month = {27 August 2021}, abstract = {Machine learning models achieve state-of-the-art performance on many supervised learning tasks. However, prior evidence suggests that these models may learn to rely on shortcut biases or spurious correlations (intuitively, correlations that do not hold in the test as they hold in train) for good predictive performance. Such models cannot be trusted in deployment environments to provide accurate predictions. While viewing the problem from a causal lens is known to be useful, the seamless integration of causation techniques into machine learning pipelines remains cumbersome and expensive. In this work, we study and extend a causal pre-training debiasing technique called causal bootstrapping (CB) under five practical confounded-data generation-acquisition scenarios (with known and unknown confounding). Under these settings, we systematically investigate the effect of confounding bias on deep learning model performance, demonstrating their propensity to rely on shortcut biases when these biases are not properly accounted for. We demonstrate that such a causal pre-training technique can significantly outperform existing base practices to mitigate confounding bias on real-world domain generalization benchmarking tasks. This systematic investigation underlines the importance of accounting for the underlying data-generating mechanisms and fortifying data-preprocessing pipelines with a causal framework to develop methods robust to confounding biases.}, author = {Sindhu Gowda and Joshi, Shalmali and Zhang, Haoran and Marzyeh Ghassemi} } @article {1682066, title = {Learning under adversarial and interventional shifts}, year = {Forthcoming}, month = {29 March 2021}, abstract = {Machine learning models are often trained on data from one distribution and deployed on others. So it becomes important to design models that are robust to distribution shifts. Most of the existing work focuses on optimizing for either adversarial shifts or interventional shifts. Adversarial methods lack expressivity in representing plausible shifts as they consider shifts to joint distributions in the data. Interventional methods allow more expressivity but provide robustness to unbounded shifts, resulting in overly conservative models. In this work, we combine the complementary strengths of the two approaches and propose a new formulation, RISe, for designing robust models against a set of distribution shifts that are at the intersection of adversarial and interventional shifts. We employ the distributionally robust optimization framework to optimize the resulting objective in both supervised and reinforcement learning settings. Extensive experimentation with synthetic and real world datasets from healthcare demonstrate the efficacy of the proposed approach.}, url = {https://arxiv.org/pdf/2103.15933.pdf}, author = {Harvineet Singh and Joshi, Shalmali and Doshi-Velez, Finale and Himabindu Lakkaraju} } @article {1668012, title = {Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making}, year = {Forthcoming}, month = {20 Jan 2022}, abstract = {\ \ \ \  Assessing the effects of a policy based on observational data from a different policy is a common problem across several high-stake decision-making domains, and several off-policy evaluation (OPE) techniques have been proposed. However, these methods largely formulate OPE as a problem disassociated from the process used to generate the data (i.e. structural assumptions in the form of a causal graph). We argue that explicitly highlighting this association has important implications on our understanding of the fundamental limits of OPE. First, this implies that current formulation of OPE corresponds to a narrow set of tasks, i.e. a specific causal estimand which is focused on prospective evaluation of policies over populations or sub-populations. Second, we demonstrate how this association motivates natural desiderata to consider a general set of causal estimands, particularly extending the role of OPE for counterfactual off-policy evaluation at the level of individuals of the population. A precise description of the causal estimand highlights which OPE estimands are identifiable from observational data under the stated generative assumptions. For those OPE estimands that are not identifiable, the causal perspective further highlights where more experimental data is necessary, and highlights situations where human expertise can aid identification and estimation. Furthermore, many formalisms of OPE overlook the role of uncertainty entirely in the estimation process. We demonstrate how specifically characterising the causal estimand highlights the different sources of uncertainty and when human expertise can naturally manage this uncertainty. We discuss each of these aspects as actionable desiderata for future OPE research at scale and in-line with practical utility.}, url = {https://arxiv.org/abs/2201.08262}, author = {Sonali Parbhoo and Joshi, Shalmali and Doshi-Velez, Finale} } @conference {1666896, title = {Counterfactually Guided Off-policy Transfer in Clinical Settings}, booktitle = {Conference for Health, Inference, and Learning (CHIL) 2022}, year = {Forthcoming}, url = {https://arxiv.org/pdf/2006.11654.pdf}, author = {Killian, TW and Ghassemi, M and Joshi, S} } @conference {1643752, title = {Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning}, booktitle = {Uncertainty in Artificial Intelligence (UAI 2022)}, year = {Forthcoming}, url = {https://arxiv.org/pdf/2107.01689.pdf}, author = {Jackson A. Killian and Lily Xu and Arpita Biswas and Tambe, Milind} } @article {https://doi.org/10.1111/nous.12486, title = {Just probabilities}, journal = {No{\^u}s}, volume = {n/a}, number = {n/a}, year = {2023}, month = {19 December 2023}, pages = {1-25}, abstract = {Abstract I defend the thesis that legal standards of proof are reducible to thresholds of probability. Many reject this thesis because it appears to permit finding defendants liable solely on the basis of statistical evidence. To the contrary, I argue {\textendash} by combining Thomson{\textquoteright}s (1986) causal analysis of legal evidence with formal methods of causal inference {\textendash} that legal standards of proof can be reduced to probabilities, but that deriving these probabilities involves more than just\ statistics.}, doi = {https://doi.org/10.1111/nous.12486}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/nous.12486}, author = {Lee-Stronach, Chad} } @article {1688841, title = {Making machine learning matter to clinicians: model actionability in medical decision-making}, journal = {NPJ Digital Medicine}, volume = {6}, number = {1}, year = {2023}, pages = {7}, abstract = {Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the performance of ML models in silico and usefulness at the point of care. One lens to use to evaluate models during early development is actionability, which is currently undervalued. We propose a metric for actionability intended to be used before the evaluation of calibration and ultimately decision curve analysis and calculation of net benefit. Our metric should be viewed as part of an overarching effort to increase the number of pragmatic tools that identify a model{\textquoteright}s possible clinical impacts.}, url = {https://www.nature.com/articles/s41746-023-00753-7}, author = {Daniel E. Ehrmann and Joshi, Shalmali and Sebastian D. Goodfellow and Mjaye L. Mazwi and Danny Eytan} } @conference {1664795, title = {Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks}, booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)}, year = {2023}, author = {Arpita Biswas and Jackson A Killian and Paula Rodriguez Diaz and Susobhan Ghosh and Tambe, Milind} } @conference {1664793, title = {Robust Planning over Restless Groups: Engagement Interventions for a Large-Scale Maternal Telehealth Program}, booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023)}, year = {2023}, author = {Jackson A. Killian and Arpita Biswas and Lily Xu and Shresth Verma and Vineet Nair and Aparna Taneja and Neha Madhiwala and Aparna Hedge and Paula Rodriguez Diaz and Sonja Johnson-Yu and Tambe, Milind} } @proceedings {1668017, title = {Exploring Counterfactual Explanations through the lens of Adversarial Examples: A Theoretical and Empirical Analysis.}, journal = {International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2022}, abstract = {\ \ \ \  As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a deeper understanding of these explanations is still lacking. In this work, we systematically analyze counterfactual explanations through the lens of adversarial examples. We do so by formalizing the similarities between popular counterfactual explanation and adversarial example generation methods identifying conditions when they are equivalent. We then derive the upper bounds on the distances between the solutions output by counterfactual explanation and adversarial example generation methods, which we validate on several real-world data sets. By establishing these theoretical and empirical similarities between counterfactual explanations and adversarial examples, our work raises fundamental questions about the design and development of existing counterfactual explanation algorithms.}, url = {https://arxiv.org/abs/2106.09992}, author = {Martin Pawelczyk and Chirag Agarwal and Joshi, Shalmali and Sohini Upadhyay and Himabindu Lakkaraju} } @conference {1643754, title = {Ranked Prioritization of Groups in Combinatorial Bandit Allocation}, booktitle = {31st International Joint Conference on Artificial Intelligence (IJCAI 2022)}, year = {2022}, url = {https://arxiv.org/abs/2205.05659}, author = {Lily Xu and Arpita Biswas and Fang, Fei and Tambe, Milind} } @article {1628581, title = {Towards Fair Recommendation in Two-Sided Platforms}, journal = {ACM Transactions on the Web (TWEB)}, volume = {16}, number = {2}, year = {2022}, pages = {1-34}, url = {https://dl.acm.org/doi/10.1145/3503624}, author = {Arpita Biswas and Gaurav Kumar Patro and Niloy Ganguly and Krishna P. Gummadi and Abhijnan Chakraborty} } @conference {1628578, title = {On Achieving Leximin Fairness and Stability in Many-to-One Matchings}, booktitle = {Proceedings of the International Conference on Autonomous Agents and Multiagent Systems as an extended abstract (AAMAS 2022)}, year = {2022}, url = {https://arxiv.org/abs/2009.05823}, author = {Shivika Narang and Arpita Biswas and Narahari Yadati} } @conference {1628574, title = {Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems}, booktitle = {Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022)}, year = {2022}, url = {https://arxiv.org/abs/2103.04730}, author = {Mate, Aditya and Arpita Biswas and Christoph Siebenbrunner and Susobhan Ghosh and Tambe, Milind} } @article {1682051, title = {Towards robust and reliable algorithmic recourse}, journal = {Advances in Neural Information Processing Systems}, volume = {34}, year = {2021}, pages = {16926-16937}, abstract = {As predictive models are increasingly being deployed in high-stakes decision making (eg, loan approvals), there has been growing interest in post-hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption that the underlying predictive model does not change. However, in practice, models are often regularly updated for a variety of reasons (eg, dataset shifts), thereby rendering previously prescribed recourses ineffective. To address this problem, we propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts. To the best of our knowledge, this work proposes the first ever solution to this critical problem. We also carry out theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts: 1) We quantify the probability of invalidation for recourses generated without accounting for model shifts. 2) We prove that the additional cost incurred due to the robust recourses output by our framework is bounded. Experimental evaluation on multiple synthetic and real-world datasets demonstrates the efficacy of the proposed framework.}, url = {https://proceedings.neurips.cc/paper/2021/file/8ccfb1140664a5fa63177fb6e07352f0-Paper.pdf}, author = {Sohini Upadhyay and Joshi, Shalmali and Himabindu Lakkaraju} } @proceedings {1668018, title = {Learning-to-defer for sequential medical decision-making under uncertainty}, journal = {Proceedings of the International Conference on Machine Learning: Workshop on Neglected Assumptions in Causal Inference (ICML)}, year = {2021}, abstract = {Learning-to-defer is a framework to automatically defer decision-making to a human expert when ML-based decisions are deemed unreliable. Existing learning-to-defer frameworks are not designed for sequential settings. That is, they defer at every instance independently, based on immediate predictions, while ignoring the potential long-term impact of these interventions. As a result, existing frameworks are myopic. Further, they do not defer adaptively, which is crucial when human interventions are costly. In this work, we propose Sequential Learning-to-Defer (SLTD), a framework for learning-to-defer to a domain expert in sequential decision-making settings. Contrary to existing literature, we pose the problem of learning-to-defer as model-based reinforcement learning (RL) to i) account for long-term consequences of ML-based actions using RL and ii) adaptively defer based on the dynamics (model-based). Our proposed framework determines whether to defer (at each time step) by quantifying whether a deferral now will improve the value compared to delaying deferral to the next time step. To quantify the improvement, we account for potential future deferrals. As a result, we learn a pre-emptive deferral policy (i.e. a policy that defers early if using the ML-based policy could worsen long-term outcomes). Our deferral policy is adaptive to the non-stationarity in the dynamics. We demonstrate that adaptive deferral via SLTD provides an improved trade-off between long-term outcomes and deferral frequency on synthetic, semi-synthetic, and real-world data with non-stationary dynamics. Finally, we interpret the deferral decision by decomposing the propagated (long-term) uncertainty around the outcome, to justify the deferral decision.}, url = {https://arxiv.org/pdf/2109.06312.pdf}, author = {Sonali Parbhoo and Joshi, Shalmali and Doshi-Velez, Finale} } @proceedings {1668007, title = {An Empirical Framework for Domain Generalization in Clinical Settings}, journal = {Conference for Health, Inference, and Learning (CHIL) 2021}, year = {2021}, abstract = {\ \ \ \  Clinical machine learning models experience significantly degraded performance in datasets not seen during training, e.g., new hospitals or populations. Recent developments in domain generalization offer a promising solution to this problem by creating models that learn invariances across environments. In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data. We introduce a framework to induce synthetic but realistic domain shifts and sampling bias to stress-test these methods over existing non-healthcare benchmarks. We find that current domain generalization methods do not consistently achieve significant gains in out-of-distribution performance over empirical risk minimization on real-world medical imaging data, in line with prior work on general imaging datasets. However, a subset of realistic induced-shift scenarios in clinical time series data do exhibit limited performance gains. We characterize these scenarios in detail, and recommend best practices for domain generalization in the clinical setting.}, url = {https://arxiv.org/abs/2103.11163}, author = {Zhang, Haoran and Dullerud, Natalie and Seyyed-Kalantari, Laleh and Morris, Quaid and Joshi, Shalmali and Marzyeh Ghassemi} } @proceedings {1608279, title = {A Game-Theoretic Approach for Hierarchical Policy-Making}, journal = {nd International (Virtual) Workshop on Autonomous Agents for Social Good (AASG 2021)}, year = {2021}, abstract = { We present the design and analysis of a multi-level game-theoretic model of hierarchical policy-making, inspired by policy responses to the COVID-19 pandemic. Our model captures the potentially mismatched priorities among a hierarchy of policy-makers (e.g., federal, state, and local governments) with respect to two main cost components that have opposite dependence on the policy strength, such as post-intervention infection rates and the cost of policy implementation. Our model further includes a crucial third fac- tor in decisions: a cost of non-compliance with the policy-maker immediately above in the hierarchy, such as non-compliance of state with federal policies. Our first contribution is a closed-form approximation of a recently published agent-based model to com- pute the number of infections for any implemented policy. Second, we present a novel equilibrium selection criterion that addresses common issues with equilibrium multiplicity in our setting. Third, we propose a hierarchical algorithm based on best response dynamics for computing an approximate equilibrium of the hierarchical policy-making game consistent with our solution concept. Finally, we present an empirical investigation of equilibrium policy strategies in this game as a function of game parameters, such as the degree of centralization and disagreements about policy priorities among the agents, the extent of free riding as well as fairness in the distribution of costs. }, url = {https://teamcore.seas.harvard.edu/files/teamcore/files/aasg_2021_paper_9.pdf}, author = {Feiran Jia and Mate, Aditya and Zun Li and Shahin Jabbari and Mithun Chakraborty and Tambe, Milind and Michael Wellman and Vorobeychik, Yevgeniy} } @proceedings {1608274, title = {Space, Time, and Counts: Improved Human vs Animal Detection in Thermal Infrared Drone Videos for Prevention of Wildlife Poaching}, journal = {KDD 2021 Fragile Earth Workshop}, year = {2021}, author = {Puri, Anika and Bondi, Elizabeth} } @conference {1607646, title = {Q-Learning Lagrange Policies for Multi-Action Restless Bandits}, booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)}, year = {2021}, url = {https://arxiv.org/abs/2106.12024v1}, author = {Jackson A Killian and Arpita Biswas and Sanket Shah and Tambe, Milind} } @conference {1607645, title = {Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare}, booktitle = {Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021)}, year = {2021}, pages = {4036-4049}, url = {https://www.ijcai.org/proceedings/2021/556}, author = {Arpita Biswas and Gaurav Aggarwal and Varakantham, Pradeep and Tambe, Milind} } @conference {1607643, title = {Ensuring Fairness under Prior Probability Shifts}, booktitle = {AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021)}, year = {2021}, url = {https://arxiv.org/abs/2005.03474}, author = {Arpita Biswas and Suvam Mukherjee} } @conference {1607642, title = {Learning Index Policies for Restless Bandits with Application to Maternal Healthcare}, booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)}, year = {2021}, url = {http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1467.pdf}, author = {Arpita Biswas and Gaurav Aggarwal and Varakantham, Pradeep and Tambe, Milind} } @conference {1598592, title = {Transformative-fair AI for Addressing the Societal Origins of Marginalization}, year = {2021}, author = {Herman Saksono} } @conference {1598591, title = {Asset-Based Insights on Designing Fitness Promotion Techs in Boston{\textquoteright}s Low-SES Neighborhoods}, year = {2021}, abstract = { The disproportionate burden of obesity among low-socioeconomic status (SES), Black, and Latinx households underscores the ever-increasing health disparities in the United States. This epidemic can be prevented by prioritizing physical activity interventions for these populations. However, many technology-based physical activity interventions were designed for the individuals rather than for the individuals amidst their social environment. In this position paper, I report my eight-year research and technology designs processes. Although this research was not specifically guided by asset-based design principles, assets consistently emerged during the in-depth fieldwork. They include\ family relationships\ and\ caregiving communities. These assets appeared to influence the motivation to engage with health technologies and also enhance family physical activity self-efficacy. However, since my studies were not guided by asset-based design principles, some key assets may not be sufficiently identified. By participating in this workshop, I seek to collaboratively explore how to support communities to identify their assets and also how to translate assets into technology designs towards enhancing health equity. }, url = {https://scholar.harvard.edu/files/hsaksono/files/saksono-2020-asset-based_design.pdf}, author = {Herman Saksono} } @conference {1597967, title = {StoryMap: Using Social Modeling and Self-Modeling to Support Physical Activity Among Families of Low-SES Backgrounds}, booktitle = {CHI {\textquoteright}21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, year = {2021}, pages = {1-14}, publisher = {2021 Proceedings}, organization = {2021 Proceedings}, address = {Yokahama, Japan}, abstract = {Physical activity (PA) is crucial for reducing the risk of obesity, an epidemic that disproportionately burdens families of low-socioeconomic status (SES). While fitness tracking tools can increase PA awareness, more work is needed to examine (1) how such tools can help people benefit from their social environment, and (2) how reflections can help enhance PA attitudes. We investigated how fitness tracking tools for families can support social modeling and self-modeling (through reflection), two critical processes in Social Cognitive Theory. We developed StoryMap, a novel fitness tracking app for families aimed at supporting both modes of modeling. Then, we conducted a five-week qualitative study evaluating StoryMap with 16 low-SES families. Our findings contribute an understanding of how social and self-modeling can be implemented in fitness tracking tools and how both modes of modeling can enhance key PA attitudes: self-efficacy and outcome expectations. Finally, we propose design recommendations for social personal informatics tools.}, url = {https://doi.org/10.1145/3411764.3445087}, author = {Herman Saksono and Carmen Castaneda-Sceppa and Jessica A Hoffman and Magy Seif el-Nasr and Andrea Grimes Parker} } @conference {1597345, title = {An empirical framework for domain generalization in clinical settings}, booktitle = {ACM Conference on Health, Inference, and Learning}, year = {2021}, address = {Virtual Event, UA}, abstract = {Clinical machine learning models experience significantly degraded performance in datasets not seen during training, e.g., new hospitals or populations. Recent developments in domain generalization offer a promising solution to this problem by creating models that learn invariances across environments. In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data. We introduce a framework to induce synthetic but realistic domain shifts and sampling bias to stress-test these methods over existing nonhealthcare benchmarks. We find that current domain generalization methods do not achieve significant gains in out-of-distribution performance over empirical risk minimization on real-world medical imaging data, in line with prior work on general imaging datasets. However, a subset of realistic induced-shift scenarios in clinical time series data exhibit limited performance gains. We characterize these scenarios in detail, and recommend best practices for domain generalization in the clinical setting.}, author = {Zhang, Haoran and Dullerud, Natalie and Seyyed-Kalantari, Laleh and Morris, Quaid and Joshi, Shalmali and Marzyeh Ghassemi} } @proceedings {1596577, title = {Active Screening for Recurrent Diseases: A Reinforcement Learning Approach}, journal = {20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)}, year = {2021}, address = {London, UK }, abstract = {Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically select a subset of population for screening. However, given the limited number of health workers, only a small subset of the population can be visited in any given time period. Given the recurrent nature of the disease and rapid spreading, the goal is to minimize the number of infections over a long time horizon. Active screening can be formalized as a sequential combinatorial optimization over the network of people and their connections. The main computational challenges in this formalization arise from i) the combinatorial nature of the problem, ii) the need of sequential planning and iii) the uncertainties in the infectiousness states of the population.}, author = {Ou, Han-Ching and Haipeng Chen and Shahin Jabbari and Tambe, Milind} } @proceedings {1556010, title = {Beyond {\textquotedblleft}To Act or Not to Act{\textquotedblright}: Fast Lagrangian Approaches to General Multi-Action Restless Bandits}, journal = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {We present a new algorithm and theoretical results for solving Multi-action Multi-armed Restless Bandits, an important but insufficiently studied generalization of traditional Multi-armed Restless Bandits (MARBs). Multi-action MARBs are capable of handling critical problem complexities often present in AI4SG domains like anti-poaching and healthcare, but that traditional MARBs fail to capture. Limited previous work on Multi-action MARBs has only been specialized to sub-problems. Here we derive BLam, an algorithm for general Multi-action MARBs using Lagrangian relaxation techniques and convexity to quickly converge to good policies via bound optimization. We also provide experimental results comparing BLam to baselines on a simulated distributions motivated by a real-world community health intervention task, achieving up to five-times speedups over more general methods without sacrificing performance.}, author = {Jackson A. Killian and Perrault, Andrew and Tambe, Milind} } @conference {1548867, title = {Social Media Attributions in the Context of Water Crisis}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Attribution of natural disasters/collective misfortunes is a widely studied social science problem. At present, most such studies rely on surveys or external signals such as voting outcomes. Typically, these surveys are costly to conduct and often have considerable turnaround time. In contrast, procuring social media data is vastly cheaper and can be obtained at varying spatiotemporal granularity. In this paper, we describe our recent work1 that looked into the viability of estimating attributions through social media discussions. To this end, (1) we fo- cus on the 2019 Chennai water crisis, a major in- stance of recent environmental resource crisis; (2) construct a substantial corpus of 72,098 YouTube comments posted by 43,859 users on 623 videos relevant to the crisis; (3) define a novel natural language processing task of attribution tie detection; and (4) design a neural classifier that achieves a reasonable performance. We also release the first data set on this novel task and important domain.}, author = {Rupak Sarkar and Sayantan Mahinder and Hirak Sarkar and Ashiqur R. KhudaBukhsh} } @conference {1548866, title = {Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reasonable compromise between fairness and accuracy is desired. Another common approach is to limit the ability of a particular adversary who seeks to maximize parity. Unfortunately, representations produced by adversarial approaches may still retain biases as their efficacy is tied to the complexity of the adversary used during training. To this end, we theoretically establish that by limiting the mutual information between representations and protected attributes, we can assuredly control the parity of any downstream classifier. We demonstrate an effective method for controlling parity through mutual information based on contrastive information estimators and show that it outperforms other exist- ing approaches. We test our approach on UCI Adult and Heritage Health datasets and show that our approach provides more informative representations across a range of desired parity thresholds while providing strong theoretical guarantees on the parity of any downstream algorithm.}, author = {Gupta, Umang and Aaron Ferber and Dilkina, Bistra and Greg Ver Steeg} } @conference {1548865, title = {Crowd-Sourced Road Quality Mapping in the Developing World}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Road networks are among the most essential components of a country{\textquoteright}s infrastructure. By facilitating the movement and exchange of goods, people, and ideas, they support economic and cultural activity both within and across borders. Up- to-date mapping of the the geographical distribution of roads and their quality is essential in high- impact applications ranging from land use planning to wilderness conservation. Mapping presents a particularly pressing challenge in developing countries, where documentation is poor and disproportionate amounts of road construction are expected to occur in the coming decades. We present a new crowd-sourced approach capable of assessing road quality and identify key challenges and opportunities in the transferability of deep learning based methods across domains.}, author = {Benjamin Choi and John Kamalu} } @conference {1548862, title = {Feature Representations for Conservation Bioacoustics: Review and Discussion}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Acoustic analysis is becoming a key element of environmental monitoring for wildlife conservation. Passive acoustic recorders can document a variety of vocal animals over large areas and long time horizons, paving the path for machine learning algorithms to identify individual species, estimate abundance, and evaluate ecosystem health. How- ever, such techniques rely on finding meaningful characterizations of calls and soundscapes, capable of capturing complex spatiotemporal, taxonomic, and behavioral structure. This article reviews existing methods for computing informative lower- dimensional features in the context of terrestrial passive acoustic monitoring, and discusses directions for further work.}, author = {Irina Tolkova} } @conference {1548861, title = {Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {The presence of {\textquotedblleft}big data{\textquotedblright} in higher education has led to the increasing popularity of predictive analytics for guiding various stakeholders on appropriate actions to support student success. In develop- ing such applications, model selection is a central issue. As such, this study presents a comprehensive examination of five commonly used machine learning models in student success prediction. Us- ing administrative and learning management system (LMS) data for nearly 2,000 college students at a public university, we employ the models to predict short-term and long-term academic success. Beyond the tradeoff between model interpretability and accuracy, we also focus on the fairness of these models with regard to different student populations. Our findings suggest that more interpretable mod- els such as logistic regression do not necessarily compromise predictive accuracy. Also, they lead to no more, if not less, prediction bias against disadvantaged student groups than complicated models. Moreover, prediction biases against certain groups persist even in the fairest model. These results thus recommend using simpler algorithms in conjunction with human evaluation in instructional and institutional applications of student success prediction when valid student features are in place.}, author = {Catherine Kung and Renzhe Yu} } @conference {1548860, title = {An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in BHollywood}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Bollywood, aka the Mumbai film industry, is one of the biggest movie industries in the world. With a current movie market share of worth 2.1 billion dollars and a target audience base of 1.2 billion people, Bollywood is a formidable entertainment force. While the entertainment impact in terms of lives that Bollywood can potentially touch is mammoth, no NLP study on social biases in Bollywood con- tent exists. We thus seek to understand social biases in a developing country through the lens of popular movies. Our argument is simple {\textendash} popular movie content reflects social norms and beliefs in some form or shape. We present our preliminary findings on a longitudinal corpus of English subtitles of popular Bollywood movies focusing on (1) social bias toward a fair skin color (2) gender biases, and (3) gender representation. We contrast our findings with a similar corpus of Hollywood movies.}, author = {Kunal Khadilkar and Ashiqur R. KhudaBukhsh} } @conference {1548859, title = {Reducing suicide contagion effect by detecting sentences from media reports with explicit methods of suicide}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Research has shown that suicide rate can increase by 13\% when suicide is not reported responsibly. For example, irresponsible reporting includes specific details that depict suicide methods. To pro- mote more responsible journalism to save lives, we propose a novel problem called {\textquotedblleft}suicide method detection{\textquotedblright}, which determines if a sentence in a news article contains a description of a suicide method. Our results show two promising approaches: a rule-based approach using category pattern matching and a BERT model with data augmentation, both of which reach over 0.9 in F- measure.}, author = {Shima Gerani and Raphael Tissot and Annie Ying and Jennifer Redmon and Artemio Rimando and Riley Hun} } @conference {1548857, title = {How can computer vision widen the evidence base around on-screen representation}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {There is strong demand for more complete and better data around diversity in the screen industries. Focusing on on-screen diversity and representation in the UK, the evidence base around representation on-screen has been narrow so far. Diversity evaluation needs to consider more than on-screen presence {\textendash} it should also consider prominence and portrayal. In this position paper, the ethics of applying computer vision to study on-screen characters is discussed via a conceptual framework of on- screen diversity metrics. Computer vision should be applied to identify character occurrences, rather than demographic classification. An illustrative ex- ample of measuring character prominence using a short video clip is shown. It concludes with four areas of applications where adopting computational methods can create a measurably more inclusive and representative broadcast landscape.}, author = {Raphael Leung} } @conference {1548856, title = {Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Sociotechnical systems within cities are now equipped with machine learning algorithms in hopes to increase efficiency and functionality by modeling and predicting trends. Machine learning algorithms have been applied in these domains to address challenges such as balancing the distribution of bikes throughout a city and identifying demand hotspots for ride sharing drivers. However, these algorithms applied to challenges in sociotechnical systems have exacerbated social inequalities due to previous bias in data sets or the lack of data from marginalized communities. In this paper, I will address how smart mobility initiatives in cities use machine learning algorithms to address challenges. I will also address how these algorithms unintentionally discriminate against features such as socioeconomic status to motivate the importance of algorithmic fairness. Using the bike sharing pro- gram in Pittsburgh, PA, I will present a position on how discrimination can be eliminated from the pipeline using Bayesian Optimization.}, author = {Katelyn Morrison} } @conference {1548855, title = {Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Nigerian English adaptation, Pidgin, has evolved over the years through multi-language code switch- ing, code mixing and linguistic adaptation. While Pidgin preserves many of the words in the normal English language corpus, both in spelling and pronunciation, the fundamental meaning of these words have changed significantly. For example, {\textquoteleft}ginger{\textquoteright} is not a plant but an expression of motivation and {\textquoteright}tank{\textquoteright} is not a container but an expression of gratitude. The implication is that the current approach of using direct English sentiment analysis of social media text from Nigeria is sub-optimal, as it will not be able to capture the semantic variation and contextual evolution in the contemporary meaning of these words. In practice, while many words in Nigerian Pidgin adaptation are the same as the standard English, the full English language based sentiment analysis models are not de- signed to capture the full intent of the Nigerian pid- gin when used alone or code-mixed. By augment- ing scarce human labelled code-changed text with ample synthetic code-reformatted text and meaning, we achieve significant improvements in sentiment scoring. Our research explores how to understand sentiment in an intrasentential code mixing and switching context where there has been significant word localization.This work presents a 300 VADER lexicon compatible Nigerian Pidgin sentiment tokens and their scores and a 14,000 gold standard Nigerian Pidgin tweets and their sentiments labels.}, author = {Wuraola Fisayo Oyewusi and Olubayo Adekanmbi and Olalekan Akinsande} } @conference {1548854, title = {European Strategy on AI: Are we truly fostering social good?}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Artificial intelligence (AI) is already part of our daily lives and is playing a key role in defining the economic and social shape of the future. In 2018, the European Commission introduced its AI strategy able to compete in the next years with world powers such as China and US, but relying on the respect of European values and fundamental rights. As a result, most of the Member States have published their own National Strategy with the aim to work on a coordinated plan for Europe. In this pa- per, we present an ongoing study on how European countries are approaching the field of Artificial Intelligence, with its promises and risks, through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans can contribute to the benefit of the whole society. This paper reports the main findings of a qualitative analysis of the investment plans reported in 15 European National Strategies.}, author = {Francesca Foffano and Teresa Scantamburlo and Atia Cort{\'e}s and Chiara Bissolo} } @conference {1548852, title = {Robust Welfare Guarantees for Decentralized Credit Organizations}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Rotating savings and credit associations (roscas) are informal financial organizations common in settings where communities have reduced access to formal financial institutions. In a rosca, a fixed group of participants regularly contribute small sums of money to a pool. This pool is then allocated periodically using lotteries or auction mechanisms. Roscas are empirically well-studied in the development economics literature. Due to their dynamic nature, however, roscas have proven challenging to examine theoretically. Theoretical analyses within economics have made strong assumptions about features such as the number or homogeneity of participants, the information they possess, their value for saving across time, or the number of rounds. This work presents an algorithmic study of roscas. We use techniques from the price of anarchy in auctions to characterize their welfare properties under less restrictive assumptions than previous work. Using the smoothness framework of [Syrgkanis and Tardos, 2013], we show that most common auction-based roscas have equilibrium welfare within a constant factor of the best possible. This evidence further rationalizes these organizations{\textquoteright} prevalence. Roscas present many further questions where algorithmic game theory may be helpful; we discuss several promising directions.}, author = {Rediet Abebe and Christian Ikeokwu and Sam Taggart} } @conference {1548851, title = {AI-based Mediation Improves Opinion Solicitation in a Large-scale Online Discussion: Experimental evidence from Kabul Municipality}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {We present a large-scale case study using agent plat- form that facilitated and gathered public opinions on internet-based town discussion. The hypothesis set to test how agent-mediated argumentative messages leads the discussion structure in a {\textquotedblleft}Issue-giving{\textquotedblright} and {\textquotedblleft}Issue-solving{\textquotedblright} themes involving human precipitants. The agent facilitation{\textquoteright}s mechanism set to dynamically react to participants by moderating and supporting on the bases of {\textquotedblleft}issue-solving{\textquotedblright} stance in both discussion types. We conducted two large- scale experiments to evaluate the influence of agent mediation while looking at elements of both discus- sion themes. The first experiment themed as a {\textquotedblleft}is- sue-giving{\textquotedblright} with 188 participants, and the second experiment set as a {\textquotedblleft}issue-solving{\textquotedblright} with 1076 citizens from Afghanistan. The goal of the first experiment is to contribute insights about the scale of the issues the residents facing at districts 1 and 2. The goal of second experiment is to contribute insights about the scale of issues and their solutions. In the first experiment, we found that the due to participants started by taking part with theme stance {\textquotedblleft}is- sue-giving{\textquotedblright} the first post of submitters were issues, hence the themed {\textquotedblleft}issue-giving{\textquotedblright} increased the number of issues but when agent started posting facilitation messages, the participants stance changed from issue-giving to issue-solving stance, while in second experiment the participants stance remain the same as the theme type.}, author = {Jawad Haqbeen and Takayuki Ito and Sofia Sahab} } @conference {1548850, title = {Fair and Interpretable Decision Rules for Binary Classification}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on two different measures of classification parity: equality of opportunity, and equalized odds. A column generation framework, with a novel formulation, is used to efficiently search over exponentially many possible rules, eliminating the need for heuristic rule mining. Compared to CART and Logistic Regression, two interpretable machine learning algorithms, our method produces interpretable classifiers that have superior performance with respect to both fair- ness metrics.}, author = {Connor Lawless and Oktay G{\"u}nl{\"u}k} } @conference {1548848, title = {Using Mobility Data to Understand and Forecast COVID19 Dynamics}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID- 19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecast- ing. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to exist- ing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.}, author = {Lijing Wang and Xue Ben and Aniruddha Adiga and Adam Sadilek and Ashish Tendulkar and Srinivasan Venkatramanan and Anil Vullikanti} } @conference {1548846, title = {Keyword Recommendation for Fair Search}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Online search engines are an extremely popular tool for seeking information. However, the results returned sometimes exhibit undesirable or even wrongful forms of bias, such as with respect to gender or race. In this paper, we consider the problem of fair keyword recommendation, in which the goal is to suggest keywords that are relevant to a user{\textquoteright}s search query, but exhibit less (or opposite) bias. We present a multi-objective method using word embedding to suggest alternate keywords for biased keywords present in a search query. We perform a qualitative analysis on pairs of subReddits from Reddit.com (e.g., r/AskMen vs. r/AskWomen, r/Republican vs. r/democrats). Our results demonstrate the efficacy of the proposed method and illustrate subtle linguistic differences between subReddits.}, author = {Harshit Mishra and Namrata Madan Nerli and Sucheta Soundarajan} } @conference {1548845, title = {Differentiable Optimal Adversaries for Learning Fair Representations}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Fair representation learning is an important task in many real-world domains, with the goal of finding a performant model that obeys fairness requirements. We present an adversarial representation learning algorithm that learns an informative representation while not exposing sensitive features. Our goal is to train an embedding such that it has good performance on a target task while not exposing sensitive information as measured by the performance of an optimally trained adversary. Our approach directly trains the embedding with these dual objectives in mind by implicitly differentiating through the optimal adversary{\textquoteright}s training procedure. To this end, we derive implicit gradients of the optimal logistic regression parameters with respect to the input training embeddings, and use the fully-trained logistic regression as an adversary. As a result, we are able to train a model without alternating min max optimization, leading to better training stability and improved performance. Given the flexibility of our module for differentiable programming, we evaluate the impact of using implicit gradients in two adversarial fairness-centric formulations. We present quantitative results on the trade-offs of target and fairness tasks in several real-world domains.}, author = {Aaron Ferber and Gupta, Umang and Greg Ver Steeg and Dilkina, Bistra} } @conference {1548840, title = {Learning Restless Bandits in Application to Call-based Preventive Care Programs for Maternal Healthcare}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {This paper focuses on learning index-based policies in rest- less multi-armed bandits (RMAB) with applications to public health concerns such as maternal health. Maternal health is a very important public health concern. It refers to the health of women during their pregnancy, childbirth, and the post- natal period. Although maternal health has received significant attention [World Health Organization, 2015], the number of maternal deaths remains unacceptably high, mainly because of the delay in obtaining adequate care [Thaddeus and Maine, 1994]. Most maternal deaths can be prevented by providing timely preventive care information. However, such information is not easily accessible by underprivileged and low-income communities. For ensuring timely information, a non-profit organization, called ARMMAN [2015], carries out a free call-based program called mMitra for spreading preventive care information among pregnant women. Enrollment in this program happens through hospitals and non-government organizations. Each enrolled woman receives around 140 automated voice calls, throughout their pregnancy period and up to 12 months after childbirth. Each call equips women with critical life-saving healthcare information. This program pro- vides support for around 80 weeks. To achieve the vision of improving the well-being of the enrolled women, it is important to ensure that they listen to most of the information sent to them via the automated calls. However, the organization observed that, for many women, their engagement (i.e., the overall time they spend listening to the automated calls) gradually decreases. One way to improve their engagement is by providing an intervention (that would involve a personal visit by health-care worker). These interventions require the dedicated time of the health workers, which is often limited. Thus, only a small fraction of the overall enrolled women can be provided with interventions during a time period. More- over, the extent to which the engagement improves upon intervention varies among individuals. Hence, it is important to carefully choose the beneficiaries who should be provided interventions at a particular time period. This is a challenging problem owing to multiple key reasons: (i) Engagement of the individual beneficiaries is un- certain and changes organically over time; (ii) Improvement in the engagement of a beneficiary post-intervention is un- certain; (iii) Decision making with respect to interventions (which beneficiaries should have intervention) is sequential, i.e., decisions at a step have an impact on the state of beneficiaries and decisions to be taken at the next step; (iv) Number of interventions are budgeted and are significantly smaller than the total number of beneficiaries. Due to the uncertainty, sequential nature of decision making, and weak dependency amongst patients through a budget, existing research [Lee et al., 2019; Mate et al., 2020; Bhattacharya, 2018] in health interventions has justifiably employed RMABs. However, existing research focuses on the planning problem assuming a priori knowledge of the underlying uncertainty model, which can be quite challenging to obtain. Thus, we focus on learning intervention decisions in absence of the knowledge of underlying uncertainty.}, author = {Arpita Biswas and Gaurav Aggarwal and Varakantham, Pradeep and Tambe, Milind} } @conference {1548834, title = {Trade-Offs between Fairness and Interpretability in Machine Learning}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {In this work, we look at cases where we want a classifier to be both fair and interpretable, and find that it is necessary to make trade-offs between these two properties. We have theoretical results to demonstrate this tension between the two requirements. More specifically, we consider a formal framework to build simple classifiers as a means to attain interpretability, and show that simple classifiers are strictly improvable, in the sense that every simple classifier can be replaced by a more complex classifier that strictly improves both fairness and accuracy.}, author = {Sushant Agarwal} } @conference {1548832, title = {Trade-Offs between Fairness and Privacy in Machine Learning}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {The concerns of fairness, and privacy, in machine learning based systems have received a lot of attention in the research community recently, but have primarily been studied in isolation. In this work, we look at cases where we want to satisfy both these properties simultaneously, and find that it may be necessary to make trade-offs between them. We prove a theoretical result to demonstrate this, which considers the issue of compatibility between fair- ness and differential privacy of learning algorithms. In particular, we prove an impossibility theorem which shows that even in simple binary classification settings, one cannot design an accurate learn- ing algorithm that is both ε-differentially private and fair (even approximately).}, author = {Sushant Agarwal} } @conference {1548830, title = {Artificial Intelligence to Inform Clinical Decision Making: A Practical Solution to An Ethical And Legal Challenge}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {This position paper discusses the challenges of al- locating legal and ethical responsibility to stake- holders when artificially intelligent systems (AISs) are used in clinical decision making and offers one possible solution. Clinicians have been identified as at risk of being subject to the tort of negligence if a patient is harmed as a result of their using an AIS in clinical decision making. An ethical model of prospective and retrospective personal moral re- sponsibility is suggested to avoid clinicians being treated as a {\textquoteleft}moral crumple zone{\textquoteright}. The adoption of risk pooling could support a shared model of re- sponsibility that could promote both prospective and retrospective personal moral responsibility whist avoiding the need for negligence claims.}, author = {Helen Smith} } @conference {1548827, title = {On Fairness and Interpretability}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Ethical AI spans a gamut of considerations. Among these, the most popular ones, fairness and interpretability, have remained largely distinct in technical pursuits. We discuss and elucidate the differences between fairness and interpretability across a variety of dimensions. Further, we develop two principles-based frameworks towards develop- ing ethical AI for the future that embrace aspects of both fairness and interpretability. First, interpretability for fairness proposes instantiating interpretability within the realm of fairness to develop a new breed of ethical AI. Second, fairness and interpretability initiates deliberations on bringing the best aspects of both together. We hope that these two frameworks will contribute to intensify- ing scholarly discussions on new frontiers of ethical AI that brings together fairness and interpretability.}, author = {Deepak P. and Sanil V. and Joemon M. Jose} } @conference {1548826, title = {Efficient COVID-19 Testing Using POMDPs}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = { A robust testing program is necessary for contain- ing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low- resource developing countries), designing an opti- mal testing program/strategy is a challenging prob- lem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symp- tomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing pro- gram{\textquoteright}s effectiveness. In this paper, we introduced a scalable Monte Carlo tree search based algorithm named DOCTOR, and use it to generate the op- timal testing strategies for the COVID-19. In our experiment, DOCTOR{\textquoteright}s strategies result in \~{}40\% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19. }, author = {Yu Liang and Amulya Yadav} } @conference {1548825, title = {The Relative Value of Facebook Advertising Data for Poverty Mapping}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Having reliable and up-to-date poverty data is a prerequisite for monitoring the United Nations Sustainable Development Goals (SDGs) and for planning effective poverty reduction interventions. Un- fortunately, traditional data sources are often out- dated or lacking appropriate disaggregation. As a remedy, satellite imagery has recently become prominent in obtaining geographically-fine-grained and up-to-date poverty estimates. Satellite data can pick up signals of economic activity by detecting light at night, it can pick up development status by detecting infrastructure such as roads, and it can pick up signals for individual household wealth by detecting different building footprints and roof types. It can, however, not look inside the house- holds and pick up signals from individuals. On the other hand, alternative data sources such as audience estimates from Facebook{\textquoteright}s advertising plat- form provide insights into the devices and inter- net connection types used by individuals in differ- ent locations. Previous work has shown the value of such anonymous, publicly-accessible advertising data from Facebook for studying migration, gender gaps, crime rates, and health, among others. In this work, we evaluate the added value of using Face- book data over satellite data for mapping socioeconomic development in two low and middle income countries {\textendash} the Philippines and India. We show that Facebook features perform roughly similar to satellite data in the Philippines with value added for ur- ban locations. In India, however, where Facebook penetration is lower, satellite data perform better.}, author = {Masoomali Fatehkia and Benjamin Coles and Ferda Ofli and Ingmar Weber} } @conference {1548823, title = {For One and All: Individual and Group Fairness in the Allocation of Indivisible Goods}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Traditionally, research into the fair allocation of indivisible goods has focused on individual fairness and group fairness. In this paper, we explore the co-existence of individual envy-freeness (i-EF) and its group counterpart, group weighted envy-freeness (g-WEF). We propose several polynomial-time algorithms that can provably achieve i-EF and g-WEF simultaneously in various degrees of approximation under three different conditions on the agents{\textquoteright} valuation functions: (i) when agents have identical additive valuation functions, i-EFX and g-WEF1 can be achieved simultaneously; (ii) when agents within a group share a common valuation function, an allocation satisfying both i-EF1 and g-WEF1 exists; and (iii) when agents{\textquoteright} valuations for goods within a group differ, we show that while maintaining i-EF1, we can achieve a 1 - 3 approximation to g-WEF1 in expectation. In addition, we introduce several novel fairness characterizations that exploit inherent group structures and their relation to individuals, such as proportional envy-freeness and group stability. We show that our algorithms can guarantee these properties approximately in polynomial time. Our results thus provide a first step into connecting individual and group fairness in the allocation of indivisible goods.}, author = {Jonathan Scarlett and Nicholas Teh and Yair Zick} } @conference {1548821, title = {On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {We analyze statistical discrimination using a multi-armed bandit model where myopic firms face candidate workers arriving with heterogeneous observable characteristics. The association between the worker{\textquoteright}s skill and characteristics is unknown ex ante; thus, firms need to learn it. In such an environment, laissez- faire may result in a highly unfair and inefficient outcome{\textemdash}myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated{\textemdash}they are never hired, and therefore, data about them is never accumulated. We proved that this problem becomes more seri- ous when the population ratio is imbalanced, as is the case in many extant discrimination problems. We consider two affirmative-action policies for solving this dilemma: One is a subsidy rule that is based on the popular upper confidence bound algorithm, and another is the Rooney Rule, which requires firms to interview at least one minority worker for each hiring opportunity. Our results indicate temporary affirmative actions are effective for statistical dis- crimination caused by data insufficiency.}, author = {Junpei Komiyama and Shunya Noda} } @conference {1548819, title = {A Material Lens to Investigate the Gendered Impact of the AI Industry}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = { Artificial Intelligence (AI), as a collection of tech- nologies, but more so as a growing component of the global mode of production, has a significant im- pact on gender, specifically gendered labour. In this position paper we argue that the dominant aspect of AI industry{\textquoteright}s impact on gender is more that the pro- duction and reproduction of epistemic biases which is the focus of contemporary research but is rather a material impact. We draw attention to how as a part of a larger economic structure the AI industry is altering the nature of work, expanding platformi- sation, and thus increasing precarity which is push- ing women out of the labour force. We state that this is a neglected concern and specific challenge worthy of attention for the AI research community. }, author = {Satyam Mohla and Bishnupriya Bagh and Anupam Guha} } @conference {1548818, title = {Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges comes from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of fauna, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.}, author = {Charles A. Kantor and Marta Skreta and Brice Rauby and L{\'e}onard Boussioux and Emmanuel Jehanno and Alexandra Luccioni and David Rolnick and Hugues Talbot} } @conference {1548811, title = {Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {People often turn to social media to comment upon and share information about major global events. Accordingly, social media is receiving increasing attention as a rich data source for understanding people{\textquoteright}s social, political and economic experiences of extreme weather events. In this paper, we con- tribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020.}, author = {Ancil Crayton and Jo{\~a}o Fonseca and Kanav Mehra and Michelle Ng and Jared Ross and Marcelo Sandoval-Casta{\~n}eda and Rachel von Gnecht} } @conference {1548809, title = {Increasing Mental Health Care Access with Persuasive Technology for Social Good}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {The alarming trend of increasing mental health problems and the global inability to find effective ways to address them is hampering both individual and societal good. Barriers to access mental health care are many and high, ranging from socio- economic inequalities to personal stigmas. This gives technology, especially technology based in artificial intelligence, the opportunity to help alleviate the situation and offer unique solutions. The multi- and interdisciplinary research on persuasive technology, which attempts to change behavior or attitudes without deception or coercion, shows promise in improving well-being, which results in increased equality and social good. This paper presents such systems with a brief overview of the field, and offers general, technical and critical thoughts on the implementation as well as impact. We believe that such technology can complement existing mental health care solutions to reduce inequalities in access as well as inequalities resulting from the lack of it.}, author = {Tine Kolenik and Matja{\v z} Gams} } @conference {1548808, title = {The Fairness of Leximin in Allocation of Indivisible Chores}, year = {2021}, abstract = {The leximin solution {\textemdash} which selects an allocation that maximizes the minimum utility, then the second minimum utility, and so forth {\textemdash} is known to provide EFX (envy-free up to any good) fairness guarantee in some contexts when allocating indivisible goods. However, it remains unknown how fair the leximin solution is when used to allocate in- divisible chores. In this paper, we demonstrate that the leximin solution can be modified to also provide compelling fairness guarantees for the allocation of indivisible chores. First, we generalize the definition of the leximin solution. Then, we show that the leximin solution finds a PROP1 (proportional up to one good) and PO (Pareto-optimal) allocation for 3 or 4 agents in the context of chores allocation with additive distinct valuations. Additionally, we prove that the leximin solution is EFX for combinations of goods and chores for agents with general but identical valuations.}, author = {Xingyu Chen and Zijie Liu} } @conference {1548807, title = {Challenges of Differentially Private Prediction in Healthcare Settings}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {Privacy-preserving machine learning is becoming increasingly important as models are being used on sensitive data such as electronic health records. Differential privacy is considered the gold standard framework for achieving strong privacy guarantees in machine learning. Yet, the performance implications of learning with differential privacy have not been characterized in the presence of time-varying hospital policies, care practices, and known class imbalance present in health data. First, we demon- strate that due to the long-tailed nature of health- care data, learning with differential privacy results in poor utility tradeoffs. Second, we demonstrate through an application of influence functions that learning with differential privacy leads to disproportionate influence from the majority group on model predictions which results in negative consequences for utility and fairness. Our results high- light important implications of differentially private learning; which focuses by design on learn- ing the body of a distribution to protect privacy but omits important information contained in the tails of healthcare data distributions.}, author = {Vinith M. Suriyakumar and Nicolas Papernot and Goldenberg, Anna and Marzyeh Ghassemi} } @conference {1548806, title = {ASSIST: Assistive Sensor Solutions for Independent and Safe Travel of Blind and Visually Impaired People}, booktitle = {IJCAI 2021 Workshop on AI for Social Good}, year = {2021}, abstract = {This paper describes the interface and testing of an indoor navigation app - ASSIST - that guides blind \& visually impaired (BVI) individuals through an indoor environment with high accuracy while augmenting their understanding of the surrounding environment. ASSIST features personalized inter- faces by considering the unique experiences that BVI individuals have in indoor wayfinding and of- fers multiple levels of multimodal feedback. After an overview of the technical approach and imple- mentation of the first prototype of the ASSIST system, the results of two pilot studies performed with BVI individuals are presented. Our studies show that ASSIST is useful in providing users with navigational guidance, improving their efficiency and (more significantly) their safety and accuracy in wayfinding indoors.}, author = {Zhigang Zhu and Vishnu Nair and Greg Olmschenk and William H. Seiple} } @article {xu_dual-mandate_2020, title = {Dual-Mandate Patrols: Multi-Armed Bandits for Green Security}, journal = {arXiv:2009.06560 [cs, stat]}, year = {2020}, note = {arXiv: 2009.06560}, abstract = {Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitzcontinuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.}, keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, url = {http://arxiv.org/abs/2009.06560}, author = {Lily Xu and Bondi, Elizabeth and Fang, Fei and Perrault, Andrew and Kai Wang and Tambe, Milind} } @article {1588679, title = {Incorporating Healthcare Motivated Constraints in Restless Bandit Based Resource Allocation}, journal = {NeurIPS 2020 Workshops: Challenges of Real World Reinforcement Learning, Machine Learning in Public Health (Best Lightning Paper), Machine Learning for Health (Best on Theme), Machine Learning for the Developing World}, year = {2020}, author = {Aviva Prins and Mate, Aditya and Jackson A Killian and Rediet Abebe and Tambe, Milind} } @proceedings {1597347, title = {Collapsing Bandits and their Application to Public Health Interventions}, journal = {Advances in Neural and Information Processing Systems (NeurIPS) }, year = {2020}, address = {Vancouver, Canada}, abstract = { We propose and study Collapsing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when an arm is played, the state is fully observed, thus {\textquotedblleft}collapsing{\textquotedblright} any uncertainty, but when an arm is passive, no observation is made, thus allowing uncertainty to evolve. The goal is to keep as many arms in the {\textquotedblleft}good{\textquotedblright} state as possible by planning a limited budget of actions per round. Such Collapsing Bandits are natural models for many healthcare domains in which health workers must simultaneously monitor patients and deliver interventions in a way that maximizes the health of their patient cohort. Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable. Our derivation hinges on novel conditions that characterize when the optimal policies may take the form of either {\textquotedblleft}forward{\textquotedblright} or {\textquotedblleft}reverse{\textquotedblright} threshold policies. (ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed form. (iii) We evaluate our algorithm on several data distributions including data from a real-world healthcare task in which a worker must monitor and deliver interventions to maximize their patients{\textquoteright} adherence to tuberculosis medication. Our algorithm achieves a 3-order-of-magnitude speedup compared to state-of-the-art RMAB techniques, while achieving similar performance. }, url = {https://arxiv.org/pdf/2007.04432.pdf}, author = {Mate, Aditya* and Killian, Jackson* and Haifeng Xu and Perrault, Andrew and Tambe, Milind} } @conference {1596589, title = {Automatically Learning Compact Quality-aware Surrogates for Optimization Problems}, booktitle = {NeurIPS (Spotlight)}, year = {2020}, address = {Vancouver, Canada}, abstract = {Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solvingthe problem using these values. Recent work has shown that including the optimization problem as a layer in the model training pipeline results in predictions of the unobserved parameters that lead to higher decision quality. Unfortunately, this process comes at a large computational cost because the optimization problem must be solved and differentiated through in each training iteration; furthermore, it may also sometimes fail to improve solution quality due to non-smoothness issues that arise when training through a complex optimization layer. To address these shortcomings, we learn a low-dimensional surrogate model of a large optimization problem by representing the feasible space in terms of meta-variables, each of which is a linear combination of the original variables. By training a low-dimensional surrogate model end-to-end, and jointly with the predictive model, we achieve: i) a large reduction in training and inference time; and ii) improved per-formance by focusing attention on the more important variables in the optimization and learning in a smoother space. Empirically, we demonstrate these improvements on a non-convex adversary modeling task, a submodular recommendation task and a convex portfolio optimization task.}, author = {Kai Wang and Bryan Wilder and Perrault, Andrew and Tambe, Milind} } @magazinearticle {1596587, title = {AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline}, journal = {AI Magazine}, year = {2020}, abstract = {With the maturing of AI and multiagent systems research, we have a tremendousopportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an importantrole in fighting social injustice and improving society.}, author = {Perrault, Andrew and Fang, Fei and Sinha, Arunesh and Tambe, Milind} } @conference {1525306, title = {Optimization of the Low-Carbon Energy Transition Under Static and Adaptive Carbon Taxes via Markov Decision Processes}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Many economists argue that a national carbon tax would be the most effective policy for incentivizing the development of low-carbon energy technologies. Yet existing models that measure the effects of a carbon tax only consider carbon taxes with fixed schedules. We propose a simple energy system transition model based on a finite-horizon Markov Decision Process (MDP) and use it to compare the carbon emissions reductions achieved by static versus adaptive carbon taxes. We find that in most cases, adaptive taxes achieve equivalent if not lower emissions trajectories while reducing the cost burden imposed by the carbon tax. However, the MDP optimization in our model adapted optimal policies to take advantage of the expected carbon tax adjustment, which sometimes resulted in the simulation missing its emissions targets. Back to AI for Social Good event }, author = {Alaisha Sharma and Jackson Killian and Perrault, Andrew} } @conference {1525305, title = {Test and Contain: A Resource-Optimal Testing Strategy for COVID-19}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { We propose a novel testing and containment strategy to limit the spread of SARS-CoV2 while minimising the impact on the social and economic fabric of countries struggling with the pandemic. Our approach recognises the fact that testing capacities in many low and middle-income countries (LMICs) are severely constrained. In this setting, we show that the best way to utilise a limited number of tests during a pandemic can be found by solving an allocation problem. Our problem formulation takes into account the heterogeneity of the population and uses pooled testing to identify and isolate individuals while prioritising key workers and individuals with a higher risk of spreading the disease. In order to demonstrate the efficacy of our strategy, we perform simulations using a network-based SIR model. Our simulations indicate that applying our mechanism to a population of 10, 000 individuals with only 1 test per day reduces the peak number of infected individuals by approximately 27\%, when compared to the scenario where no intervention is implemented, and requires at most 2\% of the population to self-isolate at any given point. Back to AI for Social Good event }, author = {Jakob Jonnerby and Philip Lazos and Edwin Lock and Francisco Marmolejo-Coss{\'\i}o and Christopher Bronk Ramsey and Divya Sridhar} } @conference {1525304, title = {Data, Power and Bias in Artificial Intelligence}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains exploring the interrelated dynamics of each and examining whether the inevitability of bias in AI training data may in fact be used for social good. We highlight the complexity associated with defining policies for dealing with bias. We also consider technical challenges in addressing issues of societal bias. Back to AI for Social Good event }, author = {Susan Leavy and Barry O{\textquoteright}Sullivan and Eugenia Siapera} } @conference {1525302, title = {Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { India accounts for 11\% of maternal deaths globally where a woman dies in childbirth every fifteen minutes. Lack of access to preventive care information is a significant problem contributing to high maternal morbidity and mortality numbers, especially in low-income households. We work with ARMMAN, a non-profit based in India, to further the use of call-based information programs by earlyon identifying women who might not engage on these programs that are proven to affect health parameters positively. We analyzed anonymized callrecords of over 300,000 women registered in an awareness program created by ARMMAN that uses cellphone calls to regularly disseminate health related information. We built robust deep learning based models to predict short term and long term dropout risk from call logs and beneficiaries{\textquoteright} demographic information. Our model performs 13\% better than competitive baselines for short-term forecasting and 7\% better for long term forecasting. We also discuss the applicability of this method in the real world through a pilot validation that uses our method to perform targeted interventions. Back to AI for Social Good event }, author = {Siddharth Nishtala and Harshavardhan Kamarthi and Divy Thakkar and Dhyanesh Narayanan and Anirudh Grama and Aparna Hegde and Ramesh Padmanabhan and Neha Madhiwalla and Suresh Chaudhary and Balaraman Ravindran and Tambe, Milind} } @conference {1525301, title = {On NLP Methods Robust to Noisy Indian Social Media Data }, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Much of the computational social science research focusing on issues faced in developing nations concentrates on web content written in a world language often ignoring a significant chunk of a corpus written in a poorly resourced yet highly prevalent first language of the region in concern. Such omissions are common and convenient due to the sheer mismatch between linguistic resources offered in a world language and its low-resource counterpart. However, the path to analyze English content generated in linguistically diverse regions, such as the Indian subcontinent, is not straight-forward either. Social science/AI for social good research focusing on Indian sub-continental issues faces two major Natural Language Processing (NLP) challenges: (1) how to extract a (reasonably clean) monolingual English corpus? (2) How to extend resources and analyses to its low-resource counterpart? In this paper1 , we share NLP methods, lessons learnt from our multiple projects, and outline future focus areas that could be useful in tackling these two challenges. The discussed results are critical to two important domains: (1) detecting peace-seeking, hostility-diffusing hope speech in the context of the 2019 India-Pakistan conflict (2) detecting user generated web-content encouraging COVID-19 health compliance. Back to AI for Social Good event }, author = {Ashiqur Khudabukhsh and Shriphani Palakodety and Jaime Carbonell} } @conference {1525299, title = {The Relationship between Gerrymandering Classification and Voter Incentives}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Gerrymandering is the process of drawing electoral district maps in order to manipulate the outcomes of elections. Increasingly, computers are involved in both drawing biased districts and attempts to measure and regulate this practice. The most highprofile proposals to measure partisan gerrymandering use past voting data to classify a map as gerrymandered (or not). Prior work studies the ability of these metrics to detect gerrymandering, but does not explore how the metrics could affect voter behavior or be circumvented via strategic voting. We show that using past voting data for this classification can affect strategyproofness by introducing a game which models the iterative sequence of voting and redrawing districts under regulation that bans outlier maps. In experiments, we show that a heuristic can find strategies for this game including on real North Carolin maps and voting data. Finally, we address questions from a recent US Supreme Court case that relate to our model. This is a summary of {\textquotedblleft}Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives{\textquotedblright} appearing in EC2020 Back to AI for Social Good event }, author = {Brian Brubach and Aravind Srinivasan and Shawn Zhao} } @conference {1525298, title = {In the Shadow of Disaster: Finding Shadows to Improve Damage Detection}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Rapid damage assessment after natural disasters is crucial for effective planning of relief efforts. Satellites with Very High Resolution (VHR) sensors can provide a detailed aerial image of the affected area, but current damage detection systems are fully- or semi-manual which can delay the delivery of emergency care. In this paper, we apply recent advancements in segmentation and change detection to detect damage given pre- and post-disaster VHR images of an affected area. Moreover, we demonstrate that segmentation models trained for this task rely on shadows by showing that (i) shadows influence false positive detections by the model, and (ii) removing shadows leads to poorer performance. Through this analysis, we aim to inspire future work to improve damage detection. Back to AI for Social Good event }, author = {Ilkin Bayramli and Bondi, Elizabeth and Tambe, Milind} } @conference {1525297, title = {Influence Maximization and Equilibrium Strategies in Election Network Games}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Social media has become an increasingly important political domain in recent years, especially for campaign advertising. In this work, we develop a linear model of advertising influence maximization in two-candidate elections from the viewpoint of a fully-informed social network platform, using several variations on classical DeGroot dynamics to model different features of electoral opinion formation. We consider two types of candidate objectives{\textemdash}margin of victory (maximizing total votes earned) and probability of victory (maximizing probability of earning the majority){\textemdash}and show key theoretical differences in the corresponding games, including advertising strategies for arbitrarily large networks and the existence of pure Nash equilibria. Finally, we contribute efficient algorithms for computing mixed equilibria in the margin of victory case as well as influence-maximizing best-response algorithms in both cases and show that in practice, as implemented on the Adolescent Health Dataset, they contribute to campaign equality by minimizing the advantage of the higherspending candidate. Back to AI for Social Good event }, author = {Anya Zhang and Perrault, Andrew} } @conference {1525295, title = {Topic Modeling Approaches for Understanding COVID-19 MisinformationSpread in Sub-Saharan Africa}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Since the start of the pandemic, the proliferation of fake news and misinformation has been a constant battle for health officials and policy makers as they work to curb the spread of COVID-19. In areas within the Global South, it can be difficult for officials to keep track of the growth of such false information and even harder to address the real concerns their communities have. In this paper, we present some techniques the AI community can offer to help address this issue. While the topics presented within this paper are not a complete solution, we believe they could complement the work government officials, healthcare workers, and NGOs are currently doing on the ground in Sub-Saharan Africa. Back to AI for Social Good event }, author = {Ezinne Nwankwo and Chinasa Okolo and Cynthia Habonimana} } @conference {1525292, title = {Inferring between-population differences in COVID-19 dynamics}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { As the COVID-19 pandemic continues, formulating targeted policy interventions supported by differential SARS-CoV2 transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York, United States. We then develop a Bayesian inference framework which leverages data on reported deaths to obtain a posterior distribution over unknown parameters and infer differences in the progression of the epidemic in the three locations. These findings highlight the role of between-population variation in formulating policy interventions. Back to AI for Social Good event }, author = {Bryan Wilder and Marie Charpingon and Jackson Killian and Ou, Han-Ching and Mate, Aditya and Shahin Jabbari and Perrault, Andrew and Angel Desai, Milind} } @conference {1525290, title = {Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers{\textquoteright} behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the first time, we demonstrate a measurable deterrence effect on real-world poaching data. We show that increased patrols in one region deter poaching in the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how adversaries should be modeled in realistic gametheoretic settings. Back to AI for Social Good event }, author = {Lily Xu and Perrault, Andrew and Plumptre, Andrew and Driciru, Margaret and Wanyama, Fred and Rwetsiba, Aggrey and Tambe, Milind} } @conference {1525287, title = {On Transparency of Machine Learning Models: A Position Paper}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { An ongoing challenge in machine learning is to improve the transparency of learning models, helping end users to build trust and defend fairness and equality while protecting individual privacy and information assets. Transparency is a timely topic given the increasing application of machine learning techniques in the real world, and yet much more progress is needed in addressing the transparency issues. We propose critical research questions on transparency-aware machine learning on two fronts: know how and know that. Know-how is concerned with searching for a set of decision objects (e.g. functions, rules, lists, and graphs) that are cognitively fluent for humans to apply and consistent with the original complex model, while know-that is concerned with gaining more in-depth understanding of the internal justification of the decisions through external constraints on accuracy, consistency, privacy, reliability, and fairness. Back to AI for Social Good event }, author = {Zhou, Yan and Murat Kantarcioglu} } @conference {1525285, title = {Preference Elicitation and Aggregation to Aid with Patient Triage during the COVID-19 Pandemic}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { During the COVID-19 pandemic, committees have been appointed to make ethically difficult triage decisions, which are complicated by the diversity of stakeholder interests involved. We propose a disciplined, automated approach to support such difficult collective decision-making. Our system aims to recommend a policy to the group that strikes a compromise between potentially conflicting individual preferences. To identify a policy that best aggregates individual preferences, our system first elicits individual stakeholder value judgements by asking a moderate number of strategically selected queries, each taking the form of a pairwise comparison posed to a specific stakeholder. We propose a novel formulation of this problem that selects which queries to ask which individuals to best inform the downstream recommendation problem. Modeling this as a multi-stage robust optimization problem, we show that we can equivalently reformulate this as a mixed-integer linear program which can be solved with off-the-shelf solvers. We evaluate the performance of our approach on the problem of recommending policies for allocating critical care beds to patients with COVID-19. We show that asking questions intelligently allows the system to recommend a policy with a much lower regret than asking questions randomly. The lower regret suggests that the system is suited to help a committee reach a better decision by suggesting a policy that aligns with stakeholder value judgments. \  \  Back to AI for Social Good event }, author = {Johnston, Caroline and Simon Blessenohl and Vayanos, Phebe} } @conference {1525282, title = {Nowcasting COVID-19 hospitalizations using Google Trends and LSTM}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The Google Trends data of some keywords have strong correlations with COVID-19 hospitalizations. We attempt to use these correlations and show an experimental procedure using a simple LSTM model to nowcast hospitalization peaks using Google Trends data. Experiments are done on French regions and on Belgium. This is a preliminary work, that would need to be tested during a (hopefully non-existing) second peak. Back to AI for Social Good event }, author = {Guillaume Derval and Vincent Fran{\c c}ois-Lavet and Pierre Schaus} } @conference {1525280, title = {Clustering of Social Media Messages for Humanitarian Aid Response during Crisis}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events. Prior work in analyzing social media data for crisis management has focused primarily on automatically identifying actionable (or, informative) crisis-related messages. In this work, we show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness and encourage the field to adopt them for their research or even deployment. We also extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well. Back to AI for Social Good event }, author = {Swati Padhee and Tanay Kumar Saha and Joel Tetreault and Alejandro Jaimes} } @conference {1525278, title = {To Warn or Not to Warn: Online Signaling in Audit Games}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { In health care organizations, a patient{\textquoteright}s privacy is threatened by the misuse of their electronic health record (EHR). To monitor privacy intrusions, logging systems are often deployed to trigger alerts whenever a suspicious access is detected. However, such mechanisms are insufficient in the face of small budgets, strategic attackers, and large false positive rates. In an attempt to resolve these problems, EHR systems are increasingly incorporating signaling, so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the data request will be audited later. In this paper, we formalize this auditing problem as a Signaling Audit Game (SAG). A series of experiments with 10 million real access events (containing over 26K alerts) from Vanderbilt University Medical Center (VUMC) demonstrate that a strategic presentation of warnings adds value in that SAGs realize significantly higher utility for the auditor than systems without signaling. Back to AI for Social Good event }, author = {Chao Yan and Haifeng Xu and Vorobeychik, Yevgeniy and Bo Li and Daniel Fabbri and Bradley A. Malin} } @conference {1525276, title = {Towards Automatic Generation of Context-Based Abstractive Discharge Summaries for Supporting Transition of Care}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Discharge summaries are essential for the transition of patients{\textquoteright} care but often lack sufficient information. We present an attention-based model to generate discharge summaries to support communication during the transition of care from intensive care units (ICU) to community care. We trained and evaluated our approach over 500, 000 clinical progress notes. The summaries automatically generated by our model achieve a ROUGE-L of 0.83 when comparing with discharge summaries written by health professionals. We attribute the high performance to our three-step pipeline that incorporates disease and specialist contexts to enrich the summaries with relevant information based on the context of the hospital stay. Additionally, we present a novel visualization of ICU flow of care using MIMIC-III. Our promising results have the potential to improve the pipeline of hospital discharge and continuous health care. Back to AI for Social Good event }, author = {Diana Diaz and Celia Cintas and William Ogallo and Aisha Walcott-Bryant} } @conference {1525274, title = {NILMTK-Contrib: Towards reproducible state-of-the-art energy disaggregation}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Research shows that providing an appliance-wise energy breakdown can help users save up to 15\% of their energy bills. Non-intrusive load monitoring (NILM) or energy disaggregation is the task of estimating the household energy measured at the aggregate level for each constituent appliances in the household. The problem was first was introduced in the 1980s by Hart. Over the past three decades, NILM has been an extensively researched topic by researchers. NILMTK was introduced in 2014 to the NILM community in order to motivate reproducible research. Even after the introduction of the NILMTK toolkit to the community, there has been a little contribution of recent state-of-the-art algorithms back to the toolkit. In this paper, we propose a new disaggregation API, which further simplifies the process for the rapid comparison of different state-of-the-art algorithms across a wide range of datasets and algorithms. We also propose a new rewrite for writing the new disaggregation algorithms for NILMTK, which is similar to Scikitlearn. We demonstrate the power of the new API by conducting various complex experiments using the API. Back to AI for Social Good event }, author = {Rithwik Kukunuri and Nipun Batra and Ayush Pandey and Raktim Malakar and Rajat Kumar and Odysseas Krystalakos and Mingjun Zhong,} } @conference {1525262, title = {A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI \& Social Platforms}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { COVID-19 Prevention, which combines the soft approaches and best practices for public health safety, is the only recommended solution from the health science and management society side considering the pandemic era. This process must be promoted via facilitation support to collective urban awareness programs through public dialogue and collective intelligence. Moreover, support must be provided throughout the process to perform complex public deliberation to find issues and ideas within existing approaches that can result in better approaches towards prevention. In an attempt to evaluate the validity of such claims in a conflict and COVID-19-affected country like Afghanistan, we conducted a large-scale digital social experiment using conversational AI and social platforms from an info-epidemiology and an info-veillance perspective. This served as a means to uncover an underling truth, give large-scale facilitation support, extend the soft impact of discussion to multiple sites, collect, diverge, converge and evaluate a large amount of opinions and concerns from health experts, patients and local people, deliberate on the data collected and explore collective prevention approaches of COVID-19. Finally, this paper shows that deciding a prevention measure that maximizes the probability of finding the ground truth is intrinsically difficult without utilizing the support of an AI-enabled discussion systems. Back to AI for Social Good event }, author = {Jawad Haqbeen and Takayuki Ito and Sofia Sahab and Rafik Hadfi and Shun Okuhara and Nasim Saba and Murtaza Hofiani and Umar Baregzai} } @conference {1525259, title = {Corporate Social Responsibility via Multi-Armed Bandits}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { We propose a multi-armed bandit setting where each arm corresponds to a subpopulation, and pulling an arm is equivalent to granting an opportunity to this subpopulation. In this setting the decision-maker{\textquoteright}s fairness policy governs the number of opportunities each subpopulation should receive, which typically depends on the (unknown) reward from granting an opportunity to this subpopulation. The decision-maker can decide whether to provide these opportunities or pay a predefined monetary value for every withheld opportunity. The decision-maker{\textquoteright}s objective is to maximize her utility, which is the sum of rewards minus the cost of withheld opportunities. We provide a no-regret algorithm that maximizes the decisionmaker{\textquoteright}s utility and complement our analysis with an almost-tight lower bound. Full version of the paper is available at https://tinyurl.com/y7s9avud. Back to AI for Social Good event }, author = {Tom Ron and Omer Ben-Porat and Uri Shalit} } @conference {1524088, title = {A deep learning based approach for monitoring sustainable farming practices at a parcel level}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Monitoring the effectiveness of policy interventions that promote sustainable farming practices has always been a costly affair. It requires an extensive ground presence which is not always available or reliable. In this paper we present our work so far in the application of deep learning techniques to automate the identification of individual parcels (farms). Our study area is located in the central state of Madhya Pradesh in India, where the average landholding size is around 0.6 hectares per farmer. We created a methodology that uses CNN models for segmentation and Canny Edge detector for generating contours. Our future work concentrates on improving the quality of the reference data and applying additional post-processing methods. Overall, we demonstrate how deep learning could be used for providing specific agronomic advice to individual farmers across large areas and the monitoring thereof, something which is essential in mitigating the effects of climate change. Back to AI for Social Good event }, author = {Arjun Verma and Vikram Sarbajna} } @conference {1524087, title = {Using an interpretable Machine Learning approach to study the drivers of International Migration}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Globally increasing migration pressures call for new modelling approaches in order to design effective policies. It is important to have not only efficient models to predict migration flows but also to understand how specific parameters influence these flows. In this paper, we propose an artificial neural network (ANN) to model international migration. Moreover, we use a technique for interpreting machine learning models, namely Partial Dependence Plots (PDP), to show that one can well study the effects of drivers behind international migration. We train and evaluate the model on a dataset containing annual international bilateral migration from 1960 to 2010 from 175 origin countries to 33 mainly OECD destinations, along with the main determinants as identified in the migration literature. The experiments carried out confirm that: 1) the ANN model is more efficient w.r.t. a traditional model, and 2) using PDP we are able to gain additional insights on the specific effects of the migration drivers. This approach provides much more information than only using the feature importance information used in previous works. Back to AI for Social Good event }, author = {Harold Kiossou and Yannik Schenk and Fr{\'e}d{\'e}ric Docquier and Ratheil Houndji and Siegfried Nijssen and Pierre Schaus} } @conference {1524086, title = {Locating Informal Urban Settlements}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The main idea of the paper is that convolutional neural networks can be applied to very highresolution satellite imagery in order to classify New Delhi into formal (planned colony) vs. informal settlements (Jhuggi Jhopri Clusters). We show that very high-resolution satellite imagery along with convolutional neural networks can achieve high classification accuracy of 95.81\%. We find that pretrained deep learning models for computer vision trained on standard image datasets can be effective for classification of informal settlements using satellite imagery, even when there is not a significant amount of training data. Deep learning models can learn image features without hand-crafted features and when coupled with the proliferation of cloud-based computer vision services could democratize the analysis of satellite imagery for humanitarian and developmental purposes. Back to AI for Social Good event }, author = {Bob Bell and Rajesh Veeeraraghavan} } @conference {1524085, title = {Understanding the Socio-Economic Disruption in the United States during COVID-19{\textquoteright}s Early Days}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { In this paper, we collect and study Twitter communications to understand the socio-economic impact of COVID-19 in the United States during the early days of the pandemic. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. The communications reveal the ensuing panic buying and the unavailability of some essential goods, in particular toilet paper. We also observe users express their frustration in their communications as the virus spread continued. We methodically collect a total of 530,206 tweets by identifying and tracking trending COVID-related hashtags. We then group the hashtags into six main categories, namely 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all the hashtag groups and specific to each hashtag group. Our preliminary study presents a succinct and aggregated picture of people{\textquoteright}s response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis. Back to AI for Social Good event }, author = {Swaroop Gowdra and Anand Seetharam and Arti Ramesh} } @conference {1524083, title = {Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Accurately predicting the number of new COVID19 cases is critical to understanding and controlling the spread of the disease as well as effectively managing scarce resources (e.g., hospital beds, ventilators). To this end, we design a regression based ensemble learning model comprising of Linear regression, Ridge, Lasso, ARIMA, and SVR that takes the previous 14 days{\textquoteright} data into account to predict the number of new COVID-19 cases in the short-term. The ensemble model outputs the best performance by taking into account the performance of all the models. We consider data from top 50 countries around the world that have the highest number of confirmed cases between January 21, 2020 and April 30, 2020. Our results in terms of relative percentage error show that the ensemble method provides superior prediction performance for a vast majority of these countries with less than 10\% error for 5 countries and less than 40\% error for 27 countries. Back to AI for Social Good event }, author = {Raushan Raj and Anand Seetharam and Arti Ramesh} } @conference {1523384, title = {Social Simulations for Intelligently Beating COVID-19}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The COVID-19 virus has led to a world-wide crisis that requires governments and stakeholders to take far-reaching decisions with limited knowledge of their consequences. This paper presents the AS- SOCC model as a valuable decision-support tool for anticipating the consequences of possible measures by considering many interwoven aspects at the individual, group and societal level. Moreover, this paper illustrates how this model can be applied to study the effects of different testing strategies on the spread of the virus and the healthcare system. We found that excluding age groups from random testing was ineffective, while prioritizing test- ing healthcare and education workers was effective, in combination with isolating the household of an infected person. Back to AI for Social Good event }, author = {Christian Kammler and Annet Onnes and Lo ̈{\i}s Vanhe ́e and Harko Verhagen and Bart de Bruin and Paul Davidsson and Frank Dignum and Virginia Dignum and Amineh Ghorbani and Mijke van den Hurk and Maarten Jensen and Kurt Kreulen and Fabian Lorig and Luis Gustavo Ludescher and Alexander Melchior and Rene ́ Mellema and Cezara Pastrav and Tomas Sjo ̈stro ̈m} } @conference {1523382, title = {The Neglected Dualism Of Artificial Moral Agency And Artificial Legal Reasoning In AI For Social Good}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { A neglected dualism is occurring in AI for Social Good involving the lack of encompassing both the role of artificial moral agency and artificial legal reasoning in advanced AI systems. Efforts by AI researchers and AI developers have tended to focus on how to craft and embed artificial moral agents to guide moral decision making when an AI system is operating in the field but have not also focused on and coupled the use of artificial legal reasoning capabilities, which is equally necessary for robust moral and legal outcomes. This paper addresses this problematic neglect and offers insights to overcome a substantive prevailing weakness and vulnerability. Back to AI for Social Good event }, author = {Dr. Lance B. Eliot} } @conference {1523379, title = {Sequential Fair Allocation of Limited Resources under Stochastic Demands}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Our work here is motivated by a problem faced by our lo- cal food-bank (Food Bank for the Southern Tier of New York (FBST)) in operating their mobile food pantry program. Every day, FBST uses a truck to deliver food supplies directly to distribution sites (soup kitchens/pantries/etc.). When the truck arrives at a site, the operator observes the demand there and chooses how much to allocate before moving to the next site. The number of people assembling at each site changes from day to day, and the operator typically does not know the demand of later sites (but has a sense of the demand distribution based on previous visits). Finally, the amount of food in the truck is usually insufficient to meet the total demand, and so the operator must under-allocate at each site, while trying to be fair across all sites. The question is: What is a fair allocation here, and how can it be computed? In offline problems, where demands (more generally, utility functions) for all agents are known to the principal, there are many well-studied notions of fair allocation of limited re- sources. A relevant notion in our context is that a fair allocation is one satisfying two desiderata: pareto-efficiency (for any agent to benefit, another must be hurt) and envy-freeness (no agent prefers an allocation received by another). This definition draws its importance from the fact that in many al- location settings, it is both known to be achievable, and also to encompass other natural desiderata (in particular, proportionality, wherein each agent{\textquoteright}s utility is at least that achieved under equal allocation). In particular, when goods are divisible, then for a large class of utility functions, an allocation satisfying both is easily computed (via a convex optimization program) by maximizing the Nash Social Welfare (NSW) objective subject to allocation constraints. Many settings, much like the FBST operating their mo- bile food pantry, have principals make decisions online, with incomplete knowledge on the demands for agents to come. However, these principals have access to historical data al- lowing them to generate demand histograms for each agent. Designing allocation algorithms in this setting necessitates utilizing the Bayesian information of the demand distribution to ensure equitable access to the resource, while adapting to the online realization of demands as it unfolds. Guaranteeing pareto-efficiency and envy-freeness simultaneously is impossible in this setting. However, it is important to develop algorithms which achieve probabilistic version of fairness by utilizing the distributional knowledge to develop algorithms that are approximately fair. Back to AI for Social Good event }, author = {Sean R. Sinclair and Gauri Jain and Siddhartha Banerjee and Christina Lee Yu} } @conference {1523378, title = {Finding Fair and Efficient Allocations When Valuations Don{\textquoteright}t Add Up}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents whose preferences correspond to matroid rank functions. This is a versatile valuation class, with several desirable properties (monotonicity, submodularity) which naturally models several real-world domains. We use these properties to our advantage: first, we show that when agent valuations are matroid rank functions, a socially optimal (i.e. utilitarian social welfare-maximizing) allocation that achieves envy-freeness up to one item (EF1) exists and is computationally tractable. We also prove that the Nash welfare-maximizing and the leximin allocations both exhibit this fair- ness/efficiency combination, by showing that they can be achieved by minimizing any symmetric strictly convex function of agents{\textquoteright} valuations over utilitarian optimal outcomes. Moreover, for a subclass of these valuation functions based on maximum (unweighted) bipartite matching, we show that a leximin allocation can be computed in polynomial time. Back to AI for Social Good event }, author = {Nawal Benabbou and Mithun Chakraborty and Ayumi Igarashi and Yair Zick} } @conference {1523377, title = {Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural image analysis, their application to multi-spectral satellite images (wherein input images have a large number of channels) remains relatively unexplored. In this paper, we compare different methods of leveraging multi-band information with CNNs, demonstrating the performance of all compared methods on the task of semantic segmentation of agricultural vegetation (vineyards). We show that standard industry practice of using bands selected by a domain ex- pert leads to a significantly worse test accuracy than the other methods compared. Specifically, we com- pare: using bands specified by an expert; using all available bands; learning attention maps over the input bands; and leveraging Bayesian optimisation to dictate band choice. We show that simply using all available band information already increases test time performance, and show that the Bayesian optimisation, novelly applied to band selection in this work, can be used to further boost accuracy. Back to AI for Social Good event }, author = {Sagar Vaze and Conrad James Foley and Mohamed Seddiq and Alexey Unagaev and Natalia Efremova} } @conference {1523376, title = {Sustainable Development Goal Relational Modelling: Introducing the SDG-RMF Methodology}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { A mechanism for predicting whether individual regions will meet there UN Sustainability for Development Goals (SDGs) is presented which takes into consideration the potential relationships be- tween time series associated with individual SDGs, unlike previous work where an independence assumption was made. The challenge is in identifying the relationships and then using these relationships to make SDG attainment predictions. To this end, the SDG Relational Multivariate Forecast- ing (SDG-RMF) attainment prediction methodology is presented. A multivariate forecasting mechanism for forecasting SDGs time series The results demonstrate that by considering the relationships between time series, more accurate SDG forecast predictions can be made. Back to AI for Social Good event }, author = {Yassir Alharbi and Daniel Arribas-Bel and Frans Coenen} } @conference {1523375, title = {Designing a Partnership Framework in AI for Social Good}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { While artificial intelligence (AI) has been heralded as a technology capable of solving unique problems, social good challenges are inherently structural and require the partnership of many stake- holders in order to apply AI for social good (AI4SG) in a sustainable and scaled manner. This paper explains current challenges in project implementation, surveys framework approaches, and contributes our differentiating lessons learned on scaling projects to problem domain-wide impact. The goal is to guide partnering organizations through challenges and identifying opportunities to accelerate the application of AI4SG. Back to AI for Social Good event }, author = {Caroline Trier and Lu Sevier} } @conference {1523374, title = {Progressing Social Good by Reducing Mental Health Care Inequality with Persuasive Technology}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The alarming trend of increasing mental health problems and the global inability to find effective ways to address them is hampering both individual and societal good. Barriers to access mental health care are many and high, ranging from socio- economic inequalities to personal stigmas. This gives technology, especially technology based in artificial intelligence, the opportunity to help alleviate the situation and offer unique possibilities to tackle the problem. The multi- and interdisciplinary research on persuasive technology, which attempts to change behavior or attitudes without deception or coercion, shows promise in improving wellbeing, which results in increased equality and social good. This paper presents such systems with a brief overview of the field, and offers general, technical and critical thoughts on the implementation as well as impact. We believe that such technology can complement existing mental health care solutions to reduce inequalities in access as well as inequalities resulting from the lack of it. Back to AI for Social Good event }, author = {Tine Kolenik and Matja{\v z} Gams} } @conference {1523373, title = {Algorithmic Fairness, Institutional Logics, and Social Choice}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Fairness, in machine learning research, is often conceived as an exercise in constrained optimization, based on a predefined fairness metric. We argue that this abstract model of algorithmic fairness is a poor match for the real-world, in which applications are likely to be embedded within a larger context involving multiple classes of stakeholders as well as multiple social and technical systems. We may expect multiple, competing claims around fairness coming from various stakeholders, especially in applications oriented towards social good. We propose that computational social choice is a promising framework for the integration of multiple perspectives on system outcomes in fairness- aware systems and provide an example case of personalized recommendation for a non-profit. Back to AI for Social Good event }, author = {Robin Burke and Amy Voida and Nicholas Mattei and Nasim Sonboli} } @conference {1523372, title = {Fairness and Discrimination in Mechanism Design and Machine Learning}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { As fairness and discrimination concerns permeate the design of both machine learning algorithms and mechanism design problems, we discuss differences in approaches between these two fields. We aim to bridge these two communities into a cohesive narrative that en- compasses both the large-scale capabilities of machine learning and group-focused fairness as well as the strategic incentives and utility- based notions of fairness from mechanism de- sign, showing their necessity in designing a fair pipeline. Back to AI for Social Good event }, author = {Jessie Finocchiaro and Roland Maio and Faidra Monachou and Gourab K Patro and Manish Raghavan and Ana-Andreea Stoica and Stratis Tsi} } @conference {1523371, title = {Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The COVID-19 pandemic has been a major challenge to humanity, with 12.7 million confirmed cases as of July 13th, 2020 [1]. In previous work, we described how Artificial Intelligence can be used to tackle the pandemic with applications at the molecular, clinical, and societal scales [2]. In the present follow-up article, we review these three research directions, and assess the level of maturity and feasibility of the approaches used, as well as their potential for operationalization. We also summarize some commonly encountered risks and practical pitfalls, as well as guidelines and best practices for formulating and deploying AI applications at different scales. Back to AI for Social Good event }, author = {Alexandra Luccioni and Joseph Bullock and Katherine Hoffmann Pham and Cynthia Sin Nga Lam and Miguel Luengo-Oroz} } @conference {1523370, title = {Contract Design for Afforestation Programs}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Trees on farms provide environmental benefits to society and improve agricultural productivity for farmers. We study incentive schemes for afforestation on farms through the lens of contract theory, designing conditional cash transfer schemes that encourage farmers to sustain tree growth. We capture the tree growth process as a Markov chain whose evolution is affected by the agent{\textquoteright}s (farmer) actions {\textendash} e.g., investing costly effort or cutting the tree for firewood. The principal has imperfect information about the agent{\textquoteright}s costs and actions taken, and wants to maximize long-run tree survival with minimal payment. We show how to calculate the optimal contract structure in our model: notably, it can involve time-varying payments and may incentivize the agent to join the program but abandon it prematurely. Back to AI for Social Good event }, author = {Nicole Immorlica and Wanyi Li and Brendan Lucier} } @conference {1523369, title = {Reducing Word Embedding Bias Using Learned Latent Structure}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Word embeddings learned from collections of data have demonstrated a significant level of biases. When these embeddings are used in machine learn- ing tasks it often amplifies the bias. We propose a debiasing method that uses (Figure 1) a hybrid classification - variational autoencoder network. In this work, we developed a semi-supervised classification algorithm based on variational autoencoders which learns the latent structure within the dataset and then based on learned latent structure adaptively re-weights the importance of certain data points while training. Experimental results have shown that the proposed approach works better than existing SoTA methods for debiasing word embeddings. Back to AI for Social Good event }, author = {Harshit Mishra} } @conference {1523368, title = {Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients{\textquoteright} equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver. Back to AI for Social Good event }, author = {Golnoosh Farnadi and Behrouz Babaki and Margarida Carvalho} } @conference {1523364, title = {Ethically Sourced Modeling: A Framework for Mitigating Bias in AI Projects within the US Government}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The increasingly widespread use of Natural Language Processing (NLP) in AI applications must be continually monitored for biases and false associations, especially those surrounding protected or disadvantaged classes of people. We discuss methods and algorithms used to mitigate such biases and their weak points, using real world examples in civilian agencies of the US government. Back to AI for Social Good event }, author = {Melanie Laffin} } @conference {1523363, title = {Flowering density estimation from aerial imagery for automated pineapple flower counting}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Deep Learning is changing the face of agriculture. Combined with high-resolution aerial imagery, these methods enable farmers to understand and manage their farms with previously unseen precision and efficiency. Beyond reducing costs for an industry already under significant economic stress, these advances have key environmental benefits as well: maximizing production, reducing waste, anticipating disruptions to supply chains, and limiting the use of chemicals and water through targeted application. Our approach uses a U-net based neural network to predict the density of flowering pineapple plants from aerial imagery, enabling farmers to optimize their harvesting schedule. Back to AI for Social Good event }, author = {Jennifer Hobbs and Robert Paull and Bernard Markowicz and Greg Rose} } @conference {1523361, title = {Edu2Com: an anytime algorithm to form student teams in companies.}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { In this paper we consider the problem of forming student teams adequate for company internship tasks. First, we provide a formalisation of the Feasible Team-For-Task Allocation Problem, and show the computational hardness of solving it optimally. Thereafter, we propose Edu2Com, an any- time heuristic algorithm that generates an initial team allocation that is then improved in an iterative process. Finally, we conduct a systematic evaluation and show that Edu2Com manages to (a) out- perform CPLEX in computation time, and (b) reach optimality, in the experiments considered. Back to AI for Social Good event }, author = {Athina Georgara and Carles Sierra and Juan A. Rodr ́{\i}guez-Aguilar} } @conference {1523356, title = {Forecasting Task-Shares and Characterizing Occupational Change across Industry Sectors}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Artificial Intelligence (AI) has started to transform our economy and society, more specifically, AI has the potential to make both labor and machines more productive while displacing certain human tasks and simultaneously introducing new tasks into the economy. Using online job postings data, this pa- per proposes novel methodologies to characterize the dynamic evolution of occupational task-share demands across different industries in the U.S. la- bor market and estimates the implied US-$ market values for skills. The paper develops a multi-variate and multi-step long short term memory (LSTM) network architecture to estimate 12-month and 24- month ahead forecasts of task-shares with 10\% root mean-squared error. The industry-specific insights on occupation evolution and forecasts on task- shares will facilitate the policy-makers and strategy leaders{\textquoteright} decision-making to transform the current workforce for the future. Back to AI for Social Good event }, author = {Subhro Das and Sebastian Steffen and Prabhat Reddy and Brynjolfsson, Erik and Martin Fleming} } @conference {1523354, title = {Using AI to help healthcare professionals stay up-to-date with medical research}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Staying up-to-date with current medical research can be a challenge for doctors and other medi- cal decision-makers. Systematic reviews are one of the key tools that doctors use to stay informed. These are meta-analyses of all the relevant research with the intention of answering one specific ques- tion within the healthcare domain. Cochrane pro- duces systematic reviews of medical research that are globally considered as a gold standard for high- quality healthcare information. However, because of the high volume of papers published and the fact that Cochrane{\textquoteright}s review authors are volunteers, it can take up to three years to write and publish one of these reviews. Our research focuses on speeding up this process. We propose a hybrid human-AI system to establish the topical area of a newly pub- lished paper faster, easing the process of searching for papers to include in a review. Back to AI for Social Good event }, author = {Kim de Bie and Nishant Kishore and Anthony Rentsch and Pablo Rosado and Andrea Sipka} } @conference {1523136, title = {ECO: Using AI for Everyday Armed Conflict Analysis}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, time consuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus our model contributes to the incorporation of technology in the peace building and conflict resolution sector. Back to AI for Social Good event }, author = {Anusua Trivedi and Kate Keator and Avirishu Verma and Rahul Dodhia and Juan Lavista Ferres} } @proceedings {1523134, title = {Green is the new Black: Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest}, journal = {AI for Social Good Workshop}, year = {2020}, abstract = { Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of burn scars. Recent advances in remote-sensing and availability of multimodal data offer a viable solution to this mapping problem. However, the task to segment burn marks is difficult because of its indistinguishably with similar looking land patterns, severe fragmented nature of burn marks and partially labelled noisy datasets. In this work we present AmazonNET {\textendash} a convolutional based network that allows extracting of burn patters from multimodal remote sensing images. The network consists of UNet- a well-known encoder decoder type of architecture with skip connections. The proposed framework utilises stacked RGB-NIR channels to segment burn scars from the pastures by training on a new weakly labelled noisy dataset from Amazonia. Our model illustrates superior performance by correctly identifying partially labelled burn scars and rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively utilise deep learning based segmentation models in multimodal burn scar identification. Back to AI for Social Good event }, author = {Satyam Mohla and Sidharth Mohla and Anupam Guha} } @conference {1522847, title = {Whither Fair Clustering?}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the- art in fair clustering has been quite parochial in out- look. We posit that widening the normative prin- ciples to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance. Back to AI for Social Good event }, author = {Deepak P} } @conference {1522797, title = {Enhancing Seismic Resilience of Water Pipe Networks}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { As disasters such as earthquakes and floods be- come more frequent and detrimental, it is increas- ingly important that water infrastructure resilience be strategically enhanced to support post-disaster functionality and recovery. In this paper, we focus on the problem of strategically building seismic- resilient pipe networks to ensure direct water sup- ply to critical customers and certain proximity to water sources for residential areas, which we for- malize as the Steiner network problem with cov- erage constraints. We present an efficient mixed- integer linear program encoding to solve the prob- lem. We also investigate the problem of planning partial network installments to maximize efficiency over time and propose an effective sequential plan- ning algorithm to solve it. We evaluate our algo- rithms on synthetic water networks and apply them to a case study on a water service zone in Los An- geles, which demonstrate the effectiveness of our methods for large-scale real-world applications. Back to AI for Social Good event }, author = {Taoan Huang and Dilkina, Bistra} } @conference {1522796, title = {SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { The goal of the proposed project is to transform a large transportation hub into a smart and accessible hub (SA T-Hub), with minimal infrastructure change. The societal need is significant, especially impactful for people in great need, such as those who are blind and visually impaired (BVI) or with Autism Spectrum Disorder (ASD), as well as those unfamiliar with metropolitan areas. With our inter- disciplinary background in urban systems, sensing, AI and data analytics, accessibility, and paratransit and assistive services, our solution is a hu- man-centric system approach that integrates facility modeling, mobile navigation, and user interface designs. We leverage several transportation facili- ties in the heart of New York City and throughout the State of New Jersey as testbeds for ensuring the relevance of the research and a smooth transition to real world applications. Back to AI for Social Good event }, author = {Zhigang Zhu and Jie Gong and Cecilia Feeley and Huy Vo and Tang, Hao and Arber Ruci and Bill Seiple and Zheng Yi Wu} } @conference {1522776, title = {Using AI and Satellite Earth Observation to Monitor UN Sustainable Development Indicators}, booktitle = {AI for Social Good Workshop}, year = {2020}, abstract = { There is widespread acceptance that data from earth observation satellites, combined with artificial intelligence, have the potential to play an important role to enable the quantification of the United Nations Sustainable Development Indicators (SDIs). However, building workflows that allow accurate and timely measurement of the SDIs from sub-national to global scales is proving challenging. We discuss a research program that aims to develop techniques to meet these challenges and help provide member states of the UN with effective methods of monitoring progress towards meeting the goals of the 2030 Agenda for Sustainable Development. Back to AI for Social Good event }, author = {Lynn Miller and Christoph R{\"u}diger and Geoffrey I. Webb} } @conference {1501935, title = {The Role Of Age Distribution And Family Structure On Covid-19 Dynamics: A Preliminary Modeling Assessment For Hubei And Lombardy}, booktitle = {SSRN}, year = {2020}, abstract = { Background: The COVID-19 outbreak has already caused significant mortality worldwide. As the epidemic accelerates, understanding the transmission dynamics of COVID-19 is crucial to informing national and regional policies. We develop an individual-level model for SARS-CoV2 transmission which accounts for location-dependent distributions of age and household structure. We apply our model to Hubei, China and Lombardy, Italy to analyze the impact of demographic structure on estimates for key parameters such as the rate of documentation and the reproduction number r0 for COVID-19 cases. We also assess the effectiveness of potential policies ranging from physical distancing to sheltering in place in Lombardy. Methods: Our study develops a stochastic, agent-based model for SARS-CoV2 spread. A key feature of the model is the inclusion of population-specific demographic structure, such as the distributions of age, household structure, contact across age groups, and comorbidities. We use prior estimates of these demographic features to instantiate our model for two locations: Hubei, China and Lombardy, Italy. Furthermore, we utilize the data on the number of reported deaths due to COVID-19 in both locations to estimate parameters describing location-specific variation in the transmissibility and fatality of the disease (for reasons beyond demography). The range of the parameters in our model that are consistent with reported data are used to construct plausible ranges for r0 and the rate of documentation in each location. Finally, we analyze potential policy responses in the context of Lombardy. Our analysis traces out the trade-off between adoption of physical distancing across the entire population and policies that encourage members of a specific age group to shelter at home. Results: Our estimates for r0 are comparable to the rest of the literature, with a range of 2.11{\textendash}2.27 for Hubei and 2.50-3.20 for Lombardy, suggesting higher rates of transmission in the latter. Scenarios where the case fatality rates are higher in Lombardy than Hubei by a factor of 1-5 times appear plausible given the data (even after accounting for differences in age and comorbidity distributions). We estimate the rate at which symptomatic cases are documented to be at 10.3-19.2\% in Hubei and 1.2-8\% in Lombardy, indicating that the number of undocumented cases may be even higher than has previously been estimated. Evaluation of potential policies suggests that encouraging a single age group to shelter in place is insufficient to control the epidemic by itself, but that targeted "salutary sheltering" by even 50\% of a single age group has a substantial impact when combined with adoption of physical distancing by the rest of the population. }, author = {Bryan Wilder and Marie Charpignon and Jackson A Killian and Ou, Han-Ching and Mate, Aditya and Shahin Jabbari and Perrault, Andrew and Angel Desai and Tambe, Milind and Maimuna S. Majumder} } @conference {1501932, title = {Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States.}, booktitle = {SSRN}, year = {2020}, abstract = { Background: On March 24, India ordered a 3-week nationwide lockdown in an effort to control the spread of COVID-19. While the lockdown has been effective, our model suggests that completely ending the lockdown after three weeks could have considerable adverse public health ramifications. We extend our individual-level model for COVID-19 transmission [1] to study the disease dynamics in India at the state level for Maharashtra and Uttar Pradesh to estimate the effect of further lockdown policies in each region. Specifically, we test policies which alternate between total lockdown and simple physical distancing to find "middle ground" policies that can provide social and economic relief as well as salutary population-level health effects. Methods: We use an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model is first calibrated to each region using publicly available COVID-19 death data, then implemented to simulate a range of policies. We also compute the basic reproduction number R0 and case documentation rate for both regions. Results: After the initial lockdown, our simulations demonstrate that even policies that enforce strict physical distancing while returning to normal activity could lead to widespread outbreaks in both states. However, "middle ground" policies that alternate weekly between total lockdown and physical distancing may lead to much lower rates of infection while simultaneously permitting some return to normalcy. }, author = {Mate, Aditya and Jackson A. Killian and Bryan Wilder and Marie Charpignon and Ananya Awasthi and Tambe, Milind and Maimuna S. Majumder} } @conference {1500664, title = {Who and When to Screen Multi-Round Active Screening for Network Recurrent Infectious Diseases Under Uncertainty}, booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS-20)}, year = {2020}, abstract = {Controlling recurrent infectious diseases is a vital yet complicated problem in global health. During the long period of time from patients becoming infected to finally seeking treatment, their close contacts are exposed and vulnerable to the disease they carry. Active screening (or case finding) methods seek to actively discover undiagnosed cases by screening contacts of known infected people to reduce the spread of the disease. Existing practice of active screening methods often screen all contacts of an infected person, requiring a large budget. In cooperation with a research institute in India, we develop a model of the active screening problem and present a software agent, REMEDY. This agent assists maximizing effectiveness of active screening under real world budgetary constraints and limited contact information. Our contributions are: (1) A new approach to modeling multi-round network-based screening/contact tracing under uncertainty and proof of its NP-hardness; (2) Two novel algorithms, Full- and Fast-REMEDY. Full-REMEDY considers the effect of future actions and provides high solution quality, whereas Fast-REMEDY scales linearly in the size of the network; (3) Evaluation of Full- and Fast-REMEDY on several real-world datasets which emulate human contact to show that they control diseases better than the baselines. We also show that the software agent is robust to errors in estimates of disease parameters, and incomplete information of the contact network. Our software agent is currently under review before deployment as a means to improve the efficiency of district-wise active screening for tuberculosis in India.}, author = {Ou, Han-Ching and Sinha, Arunesh and Suen, Sze-Chuan and Perrault, Andrew and Alpan Raval and Tambe, Milind} }