Algorithmic Fairness with Alice Xiang – Intel on AI Season 2, Episode 12
Alice Xiang is the Head of Fairness, Transparency, and Accountability Research at the Partnership on AI, where she leads a team of interdisciplinary researchers conducting research on algorithmic fairness, explainability, criminal justice risk assessment tools, and diversity and inclusion in the field of AI. Alice’s work sits at the intersection of social justice and AI; she seeks to tackle the ways in which algorithmic decision-making can potentially reflect or entrench societal inequities.
She previously 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. She has also given lectures and speeches at events hosted by the AAAS, IEEE, Harvard Institute of Quantitative Social Science, Tsinghua Statistical Sciences Center, and Simons Institute, among others.
Alice’s research has been published in peer-reviewed machine learning conferences, statistics journals, and law reviews. She has also been quoted in the Wall Street Journal, Fortune, the MIT Tech Review, and Wired, among others.
Alice has previously worked as an attorney, representing startups and venture capital firms, and as a data scientist developing machine learning algorithms. 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.