Victor Y. Wu

Algorithmic fairness and privacy issues are increasingly drawing both policymakers’ and the public’s attention amid rapid advances in artificial intelligence (AI). But safeguarding privacy and addressing algorithmic bias can pose a less recognized trade-off. Data minimization, while beneficial for privacy, has simultaneously made it legally, technically, and bureaucratically difficult to acquire demographic information necessary to conduct equity assessments. In this brief, we document this tension by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. We propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.