The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government

An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes, such as race and ethnicity, for bias assessment and mitigation.

Less recognized is that for nearly 50 years, the federal government has been engaged in a large-scale experiment in data minimization, limiting data sharing across federal agencies under the Privacy Act of 1974, and data collection under the Paperwork Reduction Act. This paper documents how this “privacy-bias tradeoff” has become an important battleground for fairness assessments in the U.S.