Alice
Xiang
Profile
Alice Xiang is the Global Head of AI Ethics at Sony. As the VP leading AI ethics initiatives across Sony Group, she manages the team responsible for conducting AI ethics assessments across Sony's business units and implementing Sony's AI Ethics Guidelines. In addition, as the Lead Research Scientist for AI ethics at Sony AI, Alice leads a lab of AI researchers working on cutting-edge research to enable the development of more responsible AI solutions. Alice also recently served as a General Chair for the ACM Conference on Fairness, Accountability, and Transparency (FAccT), the premier multidisciplinary research conference on these topics.
Alice previously served on the leadership team of the Partnership on AI. As the Head of Fairness, Transparency, and Accountability Research, she led a team of interdisciplinary researchers and a portfolio of multi-stakeholder research initiatives. She also served as a Visiting Scholar at Tsinghua University’s Yau Mathematical Sciences Center, where she taught a course on Algorithmic Fairness, Causal Inference, and the Law.
She has been quoted in the Wall Street Journal, MIT Tech Review, Fortune, Yahoo Finance, and VentureBeat, among others. She has given guest lectures at the Simons Institute at Berkeley, USC, Harvard, SNU Law School, among other universities. Her research has been published in top machine learning conferences, journals, and law reviews.
Alice is both a lawyer and statistician, with experience developing machine learning models and serving as legal counsel for technology companies. 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.
Message
“Our AI Ethics research team conducts cutting-edge research on fairness, transparency, and accountability in AI. Our projects aim to enable the development of more ethical AI within Sony and also to contribute to the global research discourse around AI ethics. Our goal is to make Sony a global leader in AI ethics.”
Publications
Speech AI Technologies are largely trained on publicly available datasets or by the massive web-crawling of speech. In both cases, data acquisition focuses on minimizing collection effort, without necessarily taking the data subjects’ protection or user needs into considerat…
Recent interests in causality for fair decision-making systems has been accompanied with great skepticism due to practical and epistemological challenges with applying existing causal fairness approaches. Existing works mainly seek to remove the causal effect of social categ…
As computer vision systems become more widely deployed, there is increasing concern from both the research community and the public that these systems are not only reproducing but amplifying harmful social biases. The phenomenon of bias amplification, which is the focus of t…
We propose to implicitly learn a set of continuous face-varying dimensions, without ever asking an annotator to explicitly categorize a person. We uncover the dimensions by learning on a novel dataset of 638,180 human judgments of face similarity (FAX). We demonstrate the ut…
Biases in human-centric computer vision models are often attributed to a lack of sufficient data diversity, with many demographics insufficiently represented. However, auditing datasets for diversity can be difficult, due to an absence of ground-truth labels of relevant feat…
As computer vision systems become more widely deployed, there is increasing concern from both the research community and the public that these systems are not only reproducing but amplifying harmful social biases. The phenomenon of bias amplification, which is the focus of t…
Biases in human-centric computer vision models are often attributed to a lack of sufficient data diversity, with many demographics insufficiently represented. However, auditing datasets for diversity can be difficult, due to an absence of ground-truth labels of relevant feat…
The effects of AI systems are far-reaching and affect diverse commu- nities all over the world. The demographics of AI teams, however, do not reflect this diversity. Instead, these teams, particularly at big tech companies, are dominated by Western, White, and male work- ers…
In recent years, there has been a proliferation of papers in the algorithmic fairness literature proposing various technical definitions of algorithmic bias and methods to mitigate bias. Whether these algorithmic bias mitigation methods would be permissible from a legal pers…
The risk of re-offense is considered in decision-making at many stages of the criminal justice system, from pre-trial, to sentencing, to parole. To aid decision makers in their assessments, institutions increasingly rely on algorithmic risk assessment instruments (RAIs). The…
As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often do not have access to the demographic data they feel they need to detect bias in practice. Even …
Blog
March 16, 2023 | AI Ethics
Being 'Seen' vs. 'Mis-Seen': Tensions Between Privacy and Fairness in Computer Vision
Privacy is a fundamental human right that ensures individuals can keep their personal information and activities private. In the context of computer vision, privacy concerns arise when cameras and other sensors collect personal in…
Privacy is a fundamental human right that ensures individuals can keep their personal information and activities private. In the c…
May 12, 2021 | Sony AI
Launching our AI Ethics Research Flagship
I recently joined Sony AI from the Partnership on AI, where I served on the Leadership Team and led a team of researchers focused on fairness, transparency, and accountability in AI. In that role, I had a unique vantage point in…
I recently joined Sony AI from the Partnership on AI, where I served on the Leadership Team and led a team of researchers focused…
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