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20242026
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cs.CY2026

Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safety

Hannah Cha, Neha Shukla, Solon Barocas +3

Youth increasingly turn to AI chatbots for social and emotional support, raising concerns about how these systems respond, especially in high-stakes situations. However, existing c…

cs.CY2026

The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice

Emily Black, Miranda Bogen, Logan Koepke +3

U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about ho…

cs.CY2026

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…

cs.CY2026

Effects of Generative AI Errors on User Reliance Across Task Difficulty

Jacy Reese Anthis, Hannah Cha, Solon Barocas +2

The capabilities of artificial intelligence (AI) lie along a jagged frontier, where AI systems surprisingly fail on tasks that humans find easy and succeed on tasks that humans fin…

cs.CY2026

Distinguishing Task-Specific and General-Purpose AI in Regulation

Jennifer Wang, Andrew Selbst, Solon Barocas +1

Over the past decade, policymakers have developed a set of regulatory tools to ensure AI development aligns with key societal goals. Many of these tools were initially developed in…

cs.CY2025

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor

Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn +7

In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly…