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.
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.
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.
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’s response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.
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’ 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.
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.
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.
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’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.
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.
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.
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.
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.
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.
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.
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.
The COVID-19 pandemic has been a major challenge to humanity, with 12.7 million confirmed cases as of July 13th, 2020 . In previous work, we described how Artificial Intelligence can be used to tackle the pandemic with applications at the molecular, clinical, and societal scales . 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.
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.
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.
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.
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.