"Bridging Gaps Between Metrics and Goals to Improve Societal Impacts of Machine Learning"- Serena Wang, University of California, Berkeley

3:30 pm - 4:30 pm
Virtual (Zoom)

Serena Wang, a doctoral student at the University of California, Berkeley, will deliver a talk as part of CSRAI's Young Achievers Symposium.

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"Bridging Gaps Between Metrics and Goals to Improve Societal Impacts of Machine Learning"

The increasing sophistication and proliferation of machine learning (ML) across public and private sectors has been met with both excitement and apprehension – how do we study societal impacts in this new frontier? Key to understanding the societal impacts of ML is understanding the development and deployment of such systems, which is driven by numerical metrics such as offline performance on datasets, performance on A/B tests, etc. Unfortunately, these metrics often don’t capture all developer goals or eventual societal impacts, which makes auditing and improving these systems difficult for both developers and policymakers. In this talk, Wang will discuss three approaches to bridging the gap between metrics and goals. First, she will discuss technical gaps between theory and practice in Fair ML. Second, moving beyond Fair ML, she will present technical and qualitative work on expanding the design scope of ML problem formulation. Finally, she will give a preview of ongoing work on understanding how metrics can interact with incentives in an ecosystem of agents.

About the Speaker

Serena Wang is a fifth-year Ph.D. student in computer science at the University of California, Berkeley. She has also concurrently worked at Google Research at 20% time for the last six years, where she is part of the Discrete Algorithms Group. Her research focuses on understanding and improving the long-term societal impacts of machine learning by rethinking ML algorithms and their surrounding incentives and practices. She is particularly interested in the gaps that arise between quantitative metrics and qualitative goals in algorithmic systems. She employs tools from optimization, statistics, and mechanism design. Serena is supported by the NSF Graduate Research Fellowship and the Apple Scholars in AI/ML PhD fellowship.

About the Young Achievers Symposium

The Young Achievers Symposium highlights early career researchers in diverse fields of AI for social impact. The symposium series seeks to focus on emerging research, stimulate discussions, and initiate collaborations that can advance research in artificial intelligence for societal benefit. All events in the series are free and open to the public unless otherwise noted. Penn State students, postdoctoral scholars, and faculty with an interest in socially responsible AI applications are encouraged to attend.