no code implementations • 20 Jul 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
We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches.
no code implementations • 18 Apr 2022 • McKane Andrus, Sarah Villeneuve
Most proposed algorithmic fairness techniques require access to data on a "sensitive attribute" or "protected category" (such as race, ethnicity, gender, or sexuality) in order to make performance comparisons and standardizations across groups, however this data is largely unavailable in practice, hindering the widespread adoption of algorithmic fairness.
no code implementations • 4 Feb 2021 • McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, Tom Zick
Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the graduate level remain largely unexplored.
no code implementations • 30 Oct 2020 • McKane Andrus, Elena Spitzer, Jeffrey Brown, Alice Xiang
Even with the growing variety of toolkits and strategies for working towards algorithmic fairness, they almost invariably require access to demographic attributes or proxies.
no code implementations • 10 Jul 2020 • Umang Bhatt, McKane Andrus, Adrian Weller, Alice Xiang
As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable.
no code implementations • 3 Nov 2018 • Dylan Hadfield-Menell, McKane Andrus, Gillian K. Hadfield
It has become commonplace to assert that autonomous agents will have to be built to follow human rules of behavior--social norms and laws.