Publications

Benabbou N, Chakraborty M, Igarashi A, Zick Y. Finding Fair and Efficient Allocations When Valuations Don’t Add Up, in AI for Social Good Workshop. ; 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’ 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.

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Vaze S, Foley CJ, Seddiq M, Unagaev A, Efremova N. Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks, in AI for Social Good Workshop. ; 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.

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Alharbi Y, Arribas-Bel D, Coenen F. Sustainable Development Goal Relational Modelling: Introducing the SDG-RMF Methodology, in AI for Social Good Workshop. ; 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.

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Trier C, Sevier L. Designing a Partnership Framework in AI for Social Good, in AI for Social Good Workshop. ; 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.

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Kolenik T, Gams M. Progressing Social Good by Reducing Mental Health Care Inequality with Persuasive Technology, in AI for Social Good Workshop. ; 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.

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Burke R, Voida A, Mattei N, Sonboli N. Algorithmic Fairness, Institutional Logics, and Social Choice, in AI for Social Good Workshop. ; 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.

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Finocchiaro J, Maio R, Monachou F, Patro GK, Raghavan M, Stoica A-A, Tsi S. Fairness and Discrimination in Mechanism Design and Machine Learning, in AI for Social Good Workshop. ; 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.

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Luccioni A, Bullock J, Pham KH, Lam CSN, Luengo-Oroz M. Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19, in AI for Social Good Workshop. ; 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.

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Immorlica N, Li W, Lucier B. Contract Design for Afforestation Programs, in AI for Social Good Workshop. ; 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’s (farmer) actions – e.g., investing costly effort or cutting the tree for firewood. The principal has imperfect information about the agent’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.

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Mishra H. Reducing Word Embedding Bias Using Learned Latent Structure, in AI for Social Good Workshop. ; 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.

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Farnadi G, Babaki B, Carvalho M. Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration, in AI for Social Good Workshop. ; 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’ 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.

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Laffin M. Ethically Sourced Modeling: A Framework for Mitigating Bias in AI Projects within the US Government, in AI for Social Good Workshop. ; 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.

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Hobbs J, Paull R, Markowicz B, Rose G. Flowering density estimation from aerial imagery for automated pineapple flower counting, in AI for Social Good Workshop. ; 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.

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Georgara A, Sierra C, ́ıguez-Aguilar JAR. Edu2Com: an anytime algorithm to form student teams in companies., in AI for Social Good Workshop. ; 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.

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Das S, Steffen S, Reddy P, Brynjolfsson E, Fleming M. Forecasting Task-Shares and Characterizing Occupational Change across Industry Sectors, in AI for Social Good Workshop. ; 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’ decision-making to transform the current workforce for the future.

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