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Posts
Why fairness in temporal resource allocation?
Published:
Artificial Intelligence has become an inextricable part of the human experience. With ``smart’’ tools embedded in almost every application, AI is going to make decisions for us whether we like it or not. This realization was the primary source of motivation for my research. If AI is going to be omnipresent, it is imperative to develop techniques to allow its seamless integration into society while maintaining accountability. When algorithms are used to make decisions that can affect real people[1], the analysis of their disparate impact becomes necessary for ensuring fairness. Following this thought, I started off my Ph.D. with a broad vision of improving AI transparency and fairness. In one of my initial projects, I got to examine a state-of-the-art ridesharing matching model called NeurADP[5]. NeurADP used approximate dynamic programming to efficiently match passengers to taxis with high capacity, allowing them to share rides with others. It was highly competitive, and able to service a much higher fraction of the people wanting rides. While analyzing the dynamics of this system, we discovered a large disparity in how well different regions of a city were served, with suburbs being disproportionately under-served. This, of course, raised the question: What was an acceptable trade-off for efficiency?
portfolio
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publications
FairVizard: A Visualization System for Assessing Fairness of Ride-Sharing Matching Algorithms 
Published in International Conference on Automated Planning and Scheduling (ICAPS), 2021
An interactive visualization system that let users compare different ridesharing-matching algorithms on the basis of different fairness criteria.
A Logic-Based Explanation Generation Framework for Classical and Hybrid Planning Problems
Published in Journal of Artificial Intelligence Research (JAIR), 2022
This paper presents a logic-based explanation generation framework for classical and hybrid planning problems.
VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems
Published in International Conference on Automated Planning and Scheduling (ICAPS), 2022
This paper presents a visualization framework, VizXP, that allows users to explore the explanations generated by the MRP algorithm.
Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems
Published in International Conference on Automated Planning and Scheduling (ICAPS), 2023
Improving the fairness of ridesharing systems by using a simple, incentive-based approach to improve worse-off groups over time.
DECAF: Learning to be Fair in Multi-Agent Resource Allocation
Published in RL Safety Workshop @Reinforcement Learning Conference (RLC) 2024, 2024
Learning to predict long-term fairness of actions for multi-agent resource allocation with resource constraints.
talks
Urban Ridesharing: Improving Tradeoffs Between Geographic Fairness And Efficiency
Published:
Abstract: State-of-the-art order dispatching algorithms for ridesharing use deep reinforcement learning techniques along with ILP-based optimization to achieve high efficiency in terms of service rates (proportion of passenger requests accepted). However, in pursuit of efficiency, such approaches might lead to disparity in service rates based on geographic locations, leading to some passenger groups being under-served. In this talk, I present some methods that allow us to improve group-level geographic fairness in such ridesharing systems. We propose an online method that leverages the ILP-based structure of the problem to incorporate fairness into existing matching algorithms without any retraining. Interestingly, we find that it is possible to significantly improve geographic fairness with minimal loss of overall service rate when using our methods.
teaching
Learning and Academic Support Centre (LASC)
Academic Support Mentor, Shiv Nadar University, Department of Computer Science and Engineering, 2017
As a mentor for the Learning and Academic Support Centre (LASC), I was responsible for providing assistance to over 100 undergraduate students for the course “Introduction to Computer Science and Programming”. I organized evening study sessions to help students with their coursework and introduced them to concepts in Object Oriented Programming through hands on excercises in C.
Introduction to Artificial Intelligence
Assistant in Instruction, Washington University in St. Louis, Department of Computer Science, 2022
As an assistant in instruction, I was responsible for providing one-on-one assistance to more than 130 students on coursework and homework assignments covering the mathematical foundations and application of algorithms for search, constraint satisfaction, Markov models, reinforcement learning and propositional and first order logic for the course “Introduction to Artificial Intelligence”. I graded homework assignments and exams, and conducted office hours to help students with their coursework. I also delivered guest lectures on Reinforcement Learning and conducted problem-solving sessions on Logic and MDPs.