Data collected about individuals is regularly used to make decisions that impact those same individuals. For example, statistical agencies (e.g., the U.S. Census Bureau) commonly publish statistics about groups of individuals that are then used as input to a number of critical civic decision-making procedures, including the allocation of both funding and political representation. In these settings there is a tension between the need to perform accurate allocation, in which individuals and groups receive what they deserve, and the need to protect individuals from undue disclosure of their personal information. As formal privacy methods are adopted by statistical agencies and corporations, new questions are arising about the tradeoffs between privacy protection and fairness. We are investigating these tradeoffs and devising new metrics and algorithms to support a favorable balance between these two social goods.
- Satya Kuppam, Ryan Mckenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Fair Decision Making using Privacy-Protected Data