107 citations · 177 across the 6 of their papers we have counts for
6 papers
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
Jonathan Stray, Alon Halevy, Parisa Assar +18
Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effe…
Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness
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…
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks
McKane Andrus, Sarah Dean, Thomas Krendl Gilbert +2
Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the g…
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness
McKane Andrus, Elena Spitzer, Jeffrey Brown +1
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 d…
Machine Learning Explainability for External Stakeholders
Umang Bhatt, McKane Andrus, Adrian Weller +1
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 learnin…
Legible Normativity for AI Alignment: The Value of Silly Rules
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. But human laws and norms are complex and…