Search Results - McKane Andrus

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  1. 1
    Building Human Values into Recommender Systems: An Interdisciplinary Synthesis

    Building Human Values into Recommender Systems: An Interdisciplinary Synthesis by Stray, Jonathan, Halevy, Alon, Assar, Parisa, Hadfield-Menell, Dylan, Boutilier, Craig, Ashar, Amar, Beattie, Lex, Ekstrand, Michael, Leibowicz, Claire, Connie Moon Sehat, Johansen, Sara, Kerlin, Lianne, Vickrey, David, Singh, Spandana, Vrijenhoek, Sanne, Zhang, Amy, McKane Andrus, Natali Helberger, Proutskova, Polina, Mitra, Tanushree, Vasan, Nina

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  2. 2
    Building Human Values into Recommender Systems: An Interdisciplinary Synthesis

    Building Human Values into Recommender Systems: An Interdisciplinary Synthesis by Stray, Jonathan, Halevy, Alon, Assar, Parisa, Hadfield-Menell, Dylan, Boutilier, Craig, Ashar, Amar, Bakalar, Chloe, Beattie, Lex, Ekstrand, Michael, Leibowicz, Claire, Moon Sehat, Connie, Johansen, Sara, Kerlin, Lianne, Vickrey, David, Singh, Spandana, Vrijenhoek, Sanne, Zhang, Amy, Andrus, McKane, Helberger, Natali, Proutskova, Polina, Mitra, Tanushree, Vasan, Nina

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  3. 3
    Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

    Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness by McKane Andrus, Villeneuve, Sarah

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  4. 4
    AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

    AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks by Andrus, McKane, Dean, Sarah, Gilbert, Thomas Krendl, Lambert, Nathan, Zick, Tom

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  5. 5
    "What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness

    "What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness by McKane Andrus, Spitzer, Elena, Brown, Jeffrey, Xiang, Alice

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  6. 6
    Machine Learning Explainability for External Stakeholders

    Machine Learning Explainability for External Stakeholders by Bhatt, Umang, McKane Andrus, Weller, Adrian, Xiang, Alice

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  7. 7
    AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

    AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks by McKane Andrus, Dean, Sarah, Thomas Krendl Gilbert, Lambert, Nathan, Zick, Tom

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