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Events for March 05, 2024
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CS Colloquium: Angelina Wang (Princeton University) - Operationalizing Responsible Machine Learning: From Equality Towards Equity
Tue, Mar 05, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Angelina Wang, Princeton University
Talk Title: Operationalizing Responsible Machine Learning: From Equality Towards Equity
Abstract: With the widespread proliferation of machine learning, there arises both the opportunity for societal benefit as well as the risk of harm. Approaching responsible machine learning is challenging because technical approaches may prioritize a mathematical definition of fairness that correlates poorly to real-world constructs of fairness due to too many layers of abstraction. Conversely, social approaches that engage with prescriptive theories may produce findings that are too abstract to effectively translate into practice. In my research, I bridge these approaches and utilize social implications to guide technical work. I will discuss three research directions that show how, despite the technically convenient approach of considering equality acontextually, a stronger engagement with societal context allows us to operationalize a more equitable formulation. First, I will introduce a dataset tool that we developed to analyze complex, socially-grounded forms of visual bias. Then, I will provide empirical evidence to support how we should incorporate societal context in bringing intersectionality into machine learning. Finally, I will discuss how in the excitement of using LLMs for tasks like human participant replacement, we have neglected to consider the importance of human positionality. Overall, I will explore how we can expand a narrow focus on equality in responsible machine learning to encompass a broader understanding of equity that substantively engages with societal context.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Angelina Wang is a Computer Science PhD student at Princeton University advised by Olga Russakovsky. Her research is in the area of machine learning fairness and algorithmic bias. She has been recognized by the NSF GRFP, EECS Rising Stars, Siebel Scholarship, and Microsoft AI & Society Fellowship. She has published in top machine learning (ICML, AAAI), computer vision (ICCV, IJCV), interdisciplinary (Big Data & Society), and responsible computing (FAccT, JRC) venues, including spotlight and oral presentations. Previously, she has interned with Microsoft Research and Arthur AI, and received a B.S. in Electrical Engineering and Computer Science from UC Berkeley.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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PhD Thesis Proposal - Shao-Hung Chan
Tue, Mar 05, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Shao-Hung Chan
Committee members: Sven Koenig (chair), T.K. Satish Kumar, Lars Lindemann, John Carlsson, and Daniel Harabor
Title: Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding
Time: Mar. 5th, 2:00 PM - 3:00 PM
Location: EEB 349
Abstract:
Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents that minimize the sum of path costs. Explicit Estimation Conflict-Based Search (EECBS) is a leading two-level algorithm that solves MAPF bounded-suboptimally, i.e., within some factor w away from the minimum sum of path costs C*. It uses Focal Search to find bounded-suboptimal paths on the low level and Explicit Estimation Search (EES) to resolve collisions on the high level. To solve MAPF bounded-suboptimally, EES keeps track of a lower bound LB on C* to find paths whose sum of path costs is at most w times LB. However, the costs of many paths are often much smaller than w times their minimum path costs, meaning that the sum of path costs is much smaller than w times C*. Thus, in this proposal, we hypothesize that one can improve the efficiency of EECBS via Flex Distribution. That is, one can use the flex of the path costs (that relaxes the requirement to find bounded-suboptimal paths on the low level) to reduce the number of collisions that need to be resolved on the high level while still guaranteeing to solve MAPF bounded suboptimally. We also discuss the limitations of Flex Distribution and propose some techniques to overcome them.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349
Audiences: Everyone Is Invited
Contact: CS Events
