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Building Human Values into Recommender Systems: An Interdisciplinary Synthesis


Jul 20, 2022
Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan

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AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks


Feb 04, 2021
McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, Tom Zick

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* 8 Pages 

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"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness


Oct 30, 2020
McKane Andrus, Elena Spitzer, Jeffrey Brown, Alice Xiang

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* 13 pages, 2 tables 

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Machine Learning Explainability for External Stakeholders


Jul 10, 2020
Umang Bhatt, McKane Andrus, Adrian Weller, Alice Xiang

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Legible Normativity for AI Alignment: The Value of Silly Rules


Nov 03, 2018
Dylan Hadfield-Menell, McKane Andrus, Gillian K. Hadfield

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