Resources
Automation + AI

The Privacy-Bias Trade-Off

The Privacy-Bias Trade-Off
2023
Stanford University, Institute for Human-Centered Artificial Intelligence
Author(s): 
Arushi Gupta, Victor Y. Wu, Helen Webley-Brown, Jennifer King, Daniel Ho
The Privacy-Bias Trade-Off
Source Sector(s)
Academic
Benefits Program
No items found.
Level of Government
Federal/National
Location
Format
Policy Brief

Safeguarding privacy and addressing algorithmic bias can pose an under-recognized trade-off. This brief documents tradeoffs by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. The authors propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.