8 papers
Impacts of Aggregation on Model Diversity and Consumer Utility
Kate Donahue, Manish Raghavan
Consider a marketplace of AI tools, each with slightly different strengths and weaknesses. By picking the right model for the task at hand, a user can do better than simply using t…
Statistical Guarantees in the Search for Less Discriminatory Algorithms
Chris Hays, Ben Laufer, Solon Barocas +1
U.S. discrimination law can impose liability on firms that fail to adopt a less discriminatory alternative (LDA): a decision policy that achieves the same business objectives while…
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
Charlotte Park, Kate Donahue, Manish Raghavan
Generative AI models differ from traditional machine learning tools in that they allow users to provide as much or as little information as they choose in their inputs. This flexib…
Algorithmic Monoculture and its Critics
Brian Hedden, Manish Raghavan
Algorithmic decision-making is replacing idiosyncratic human judgment in domains such as hiring, lending, and criminal justice. This shift promises increased consistency, but many…
Strategic Candidacy in Generative AI Arenas
Chris Hays, Rachel Li, Bailey Flanigan +1
AI arenas, which rank generative models from pairwise preferences of users, are a popular method for measuring the relative performance of models in the course of their organic use…
Synthetic Census Data Generation via Multidimensional Multiset Sum
Cynthia Dwork, Kristjan Greenewald, Manish Raghavan
The US Decennial Census provides valuable data for both research and policy purposes. Census data are subject to a variety of disclosure avoidance techniques prior to release in or…