Publications

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.

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.

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).

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 ‘moral crumple zone’. 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.

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.

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’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’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.

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’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 – 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.

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’ 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’ 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.

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’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—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—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.

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’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.

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.

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’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.

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.

The leximin solution — which selects an allocation that maximizes the minimum utility, then the second minimum utility, and so forth — 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.

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.