Alice Xiang is the Research Scientist leading and managing PAI’s team conducting interdisciplinary research on fairness, transparency, and accountability in AI. Core areas of her research include bridging technical and legal approaches to algorithmic bias, assessing explainability techniques in deployment, and examining risk assessment tools. Alice’s work sits at the intersection of social justice and AI; she seeks to tackle the ways in which algorithmic decision-making can further entrench societal biases and inequalities. 

She has given lectures and speeches at events hosted by the AAAS, IEEE, Simons Institute, Harvard Institute of Quantitative Social Science, Tsinghua Statistical Sciences Center, RE•WORK, and others. She most recently taught a course on “Algorithmic Fairness, Causal Inference, and the Law” at Tsinghua University’s Yau Mathematical Sciences Center, where she was a Visiting Scholar.

Alice has been quoted in Axios, Mercury News, and VentureBeat, among others, for her work on algorithmic bias and transparency, criminal justice risk assessment tools, and the limitations of AI. Her research has been published in peer-reviewed machine learning conferences, statistics journals, and law reviews. 

Prior to joining PAI, Alice worked as an attorney at Gunderson Dettmer, representing startups and venture capital firms. She has also worked in civil appellate litigation at the Department of Justice, econometrics research at the Federal Reserve, and data science at LinkedIn. Alice holds a Juris Doctor from Yale Law School, a Master’s in Development Economics from Oxford, a Master’s in Statistics from Harvard, and a Bachelor’s in Economics from Harvard. Having worked in both the public and private sectors on the legal and technical aspects of technology’s impact on society, Alice is uniquely positioned to offer insights on this intersection.