As AI systems increasingly shape critical decisions in society, ensuring fairness presents both philosophical and practical challenges. This talk begins by broadening the existing scope of normative discourse on machine learning and algorithmic decision-making. Drawing on an understanding of fair cooperation among free and equal persons as a fundamental political value as in Rawlsian theory, the talk explores how concerns about fairness and machine learning should be expanded in three key ways: (1) addressing discrimination beyond group subordination, (2) addressing equality of opportunity beyond organizational decision-making, and (3) addressing fairness beyond the equality of opportunity.

In translating these principles into practice, the challenge of evaluating automated decision systems in deployment is examined. The talk highlights how experimental designs often simplify human decision-making, potentially biasing the understanding of the impacts of algorithmic interventions. Together, these perspectives underscore the need for both conceptual expansion and rigorous evaluation to ensure that algorithmic deployments align with societal fairness.

Bio:

Lydia Liu joined Princeton University as an assistant professor in 2024. Her current research examines the theoretical foundations of machine learning and algorithmic decision-making, with a focus on societal impact and welfare.

Prior to joining Princeton she was a postdoctoral associate at Cornell University Computer Science in the Artificial Intelligence, Policy, and Practice (AIPP) initiative. Her work has be recognized with a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and an ICML Best Paper Award.

She obtained a Ph.D. in Electrical Engineering and Computer Sciences from University of California, Berkeley and a B.S.E. in Operations Research and Financial Engineering at Princeton University.

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