CSE Faculty Candidate Seminar - Angelina Wang

Name: Angelina Wang, Ph.D. student at Princeton University

Date: Tuesday, January 23, 2024 at 11:00 am

Location: Coda Building, Second Floor, Room 230 (Google Maps link)

Link: The recording of this in-person seminar will be uploaded to CSE's MediaSpace

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 difficult because technical approaches may build on too many layers of abstraction, ending up prioritizing a mathematical definition of fairness that correlates poorly to real-world constructs of fairness. On the other hand, social approaches engaging with prescriptive theories may produce findings that are too abstract to translate well into practice. In my research, I bridge these approaches and use social implications to guide technical work. I will discuss three research directions that show how although the technically convenient thing to do is consider equality acontextually, through stronger engagement with societal context we can operationalize a more equitable formulation. First, I will introduce a dataset tool that we built to analyze complex, socially-grounded forms of visual bias. Then, I will give empirical evidence to support how we should incorporate societal context in bringing intersectionality into machine learning. Finally, I will talk about how to formulate the evaluation metric of bias amplification based on more realistic assumptions about the state of the world. Overall, I will cover how we can expand a narrow focus on equality in responsible machine learning to a broader understanding of equity that substantively engages with societal context.

Bio: Angelina Wang is a computer science Ph.D. 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, and Siebel Scholarship. She has published in top machine learning (ICML, AAAI), computer vision (ICCV, IJCV), 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.