CS Events

Computer Science Department Colloquium

Navigating the Challenges of Algorithmic Decision Making: Fair and Robust Automated Systems for Low-Resource Communities

 

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Tuesday, March 28, 2023, 10:30am - 11:30am
 

Speaker: Arpita Biswas

Bio

Arpita Biswas is currently a Research Associate at the Harvard T.H. Chan School of Public Health. Prior to this, she was a CRCS Postdoctoral Fellow at the John A. Paulson School of Engineering and Applied Sciences, Harvard University. She earned her Ph.D. degree from the Department of Computer Science and Automation (CSA), Indian Institute of Science (IISc). She has been awarded the Best Thesis Prize by the Indian Academy of Engineering (INAE) 2021, Best Thesis Award by the Department of CSA, IISc (2020-2021), a Google Ph.D. Fellowship (2016-2020), and a GHCI scholarship (2018). She has been recognized as a Rising Star in STOC 2021 and in the Trustworthy ML Seminar Series for her contribution to algorithms for fair decision-making. Her broad areas of interest include Algorithmic Game Theory, Optimization, and Machine Learning. She has worked on a wide range of problems, including fair resource allocation, health intervention planning, multi-agent learning, and robust sequential decision making. More details about her can be obtained at https://sites.google.com/view/arpitabiswas/.

Location : Core 301

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Event Type: Computer Science Department Colloquium

Abstract: Automated decision-making systems play an increasingly important role in many societal decisions, such as health intervention planning, resource allocation, loan approvals, and criminal risk assessments. However, ensuring the responsible use of these systems is a challenging problem, especially for under-represented and low-resource communities. In this talk, I’ll present my research on fair and robust algorithms under resource limitations and other problem-specific constraints. The talk will cover two main themes: (1) Fair decision making in allocation and recommendation: Fairness is an important consideration in scenarios where a limited set of discrete resources is distributed among several agents, each having their own preferences. Two well-studied fairness notions in this context are envy-freeness up to one item (EF1) and maximin share (MMS). I have investigated the existence of these fairness notions under various constrained settings and developed algorithms that satisfy these fairness criteria. Further, I have used these solution concepts to quantify and promote fairness in two-sided markets (such as Netflix and Spotify) comprising customers on one side, and producers of goods/services on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the home-screen results according to the personalized preferences of users. However, our investigation reveals that such customer-centric recommendations may lead to unfair distribution of exposure among the producers, who may depend on such platforms to earn a living. I established that the two-sided fair recommendation problem can be reduced to the problem of constrained fair allocation of indivisible goods. I developed an algorithm, FairRec, that ensures maximin threshold guarantee in the exposure for a majority of the producers, and EF1 fairness for all the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring only a marginal reduction in overall recommendation quality. (2) Robust sequential intervention planning: In many public health settings, it is important to provide interventions to ensure that patients adhere to health programs, such as taking medications and periodic health checks. This is extremely crucial among low-income communities who have limited access to preventive care information and healthcare facilities. In India, a non-profit called ARMMAN provides free automated voice messages to spread preventive care information among pregnant women. One of the key challenges is to ensure that the enrolled women continue listening to the voice messages throughout their pregnancy and after childbirth. Disengagements are detrimental to their health since they often have no other source for receiving timely healthcare information. While systematic interventions, such as scheduling in-person visits by healthcare workers, can help increase their listenership, interventions are often expensive and can be provided to only a small fraction of the enrolled women. I model this as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention provided to them. I developed a robust algorithm to tackle the uncertainty in transition dynamics and can potentially reduce the number of missed voice messages by 50%. I will conclude by delving into the best practices for responsible automated decision-making and discussing future research directions. I aim to showcase my overarching vision of fostering fairness, robustness, and scalability in the realm of automated decision-making through collaboration and continuous innovation.

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Contact  David Penock

Zoom link: https://rutgers.zoom.us/j/94862779371?pwd=Vk9YNXp6Tk5XWUNlWS9ZdHF3SUQyUT09