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2020 – today
- 2024
[j1]Jonathan Stray
, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Chloé Bakalar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Xian Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan:
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. Trans. Recomm. Syst. 2(3): 20:1-20:57 (2024)- 2023
- [c8]Joice Tang, McKane Andrus, Samuel So, Udayan Tandon, Andrés Monroy-Hernández, Vera D. Khovanskaya, Sean A. Munson, Mark Zachry, Sucheta Ghoshal:
Back to " Back to Labor" : Revisiting Political Economies of Computer-Supported Cooperative Work. CSCW Companion 2023: 522-526 - 2022
- [c7]Jeffrey Brown, Tina M. Park, Jiyoo Chang, McKane Andrus, Alice Xiang, Christine Custis:
Attrition of Workers with Minoritized Identities on AI Teams. EAAMO 2022: 19:1-19:9 - [c6]McKane Andrus, Sarah Villeneuve:
Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness. FAccT 2022: 1709-1721 - [i6]McKane Andrus, Sarah Villeneuve:
Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness. CoRR abs/2205.01038 (2022) - [i5]Jonathan Stray, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy X. Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan:
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. CoRR abs/2207.10192 (2022) - 2021
- [c5]McKane Andrus, Elena Spitzer, Jeffrey Brown, Alice Xiang:
What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness. FAccT 2021: 249-260 - [i4]McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, Tom Zick:
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks. CoRR abs/2102.04255 (2021) - 2020
- [c4]McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, Tom Zick:
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks. ISTAS 2020: 72-79 - [i3]Umang Bhatt, McKane Andrus, Adrian Weller, Alice Xiang:
Machine Learning Explainability for External Stakeholders. CoRR abs/2007.05408 (2020) - [i2]McKane Andrus, Elena Spitzer, Jeffrey Brown, Alice Xiang:
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness. CoRR abs/2011.02282 (2020)
2010 – 2019
- 2019
- [c3]Dylan Hadfield-Menell, McKane Andrus, Gillian K. Hadfield:
Legible Normativity for AI Alignment: The Value of Silly Rules. AIES 2019: 115-121 - [c2]McKane Andrus, Thomas K. Gilbert:
Towards a Just Theory of Measurement: A Principled Social Measurement Assurance Program for Machine Learning. AIES 2019: 445-451 - [c1]McKane Andrus:
On Serving Two Masters: Directing Critical Technical Practice towards Human-Compatibility in AI. AIES 2019: 559-560 - 2018
- [i1]Dylan Hadfield-Menell, McKane Andrus, Gillian K. Hadfield:
Legible Normativity for AI Alignment: The Value of Silly Rules. CoRR abs/1811.01267 (2018)
Coauthor Index
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