Publications by Type: Conference Paper

In standard resource allocation problems, the designer sets the objective---such as utilitarian social welfare---that captures a societal goal and solves for the optimal allocation subject to fairness and item availability constraints. The participants, on the other hand, specify their preferences for the items being allocated, e.g., through stating how they rank the items or expressing their cardinal utility for each item. The objective function, which guides the overall allocation, is therefore determined by the designer in a top-down manner, whereas participants can only express their preferences for the items. This standard preference elicitation stage limits participants' ability to express preferences for the overall allocation, such as the level of inequality, and influence the overall objective function.

In this work, we examine whether it is possible to use this bottom-up preference elicitation stage to enable participants to express not only their preferences for individual items but also their preferences for the overall allocation, thereby indirectly influencing the objective function. We examine this question using a well-studied resource allocation problem where mm divisible items must be allocated to nn agents, who express their cardinal utilities over the items. The designer aims to optimize for the sum of the agents' utilities for the items they receive. In particular, this utilitarian objective is agnostic to the overall inequality level. We consider a setting where the agents' true utility is a function not only of their preferences for the items, but also the overall level of inequality. We model this using a popular social preference model from behavioral economics by \citeauthor{fehr1999theory}, where agents can express levels of inequality aversion.

We conduct a theoretical examination of this problem and show that there can be large gains in social welfare if the designer uses this richer inequality-aware preference model, instead of the standard inequality-agnostic preference model. Further, if we take the standard inequality-agnostic welfare as the benchmark, we show that the relative loss of welfare can be tightly bounded--shown to be independent of the number of agents and linear in the level of inequality aversion. With further assumptions on the preferences, we provide strictly tighter, distribution-free, and parametric bounds on the loss of welfare. We also discuss the worst-case drop in inequality-agnostic utility an agent might incur as a consequence of a designer allocating items using the inequality-averse preferences. We conclude with a discussion on possible designs to elicit the preferences of strategic agents over the goods and fairness. Taken together, our results argue for potentially large gains that can be obtained from using the richer social preference model and demonstrate the relatively minor losses from using the standard model, highlighting a promising avenue for using preference elicitation to empower participants to influence the overall objective function.

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.

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.

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.

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.

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.

Road networks are among the most essential components of a country’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.

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.