Recommended Citation
Elizabeth A. Brown,
Predictive Health Discrimination: Cabining AI Risk Assessment to Protect Worker Data,
36 Health Matrix
167
(2026)
Available at:
https://scholarlycommons.law.case.edu/healthmatrix/vol36/iss1/3
Abstract
Predictive health data monitoring is rapidly expanding in workplaces, outpacing legal protections and exposing workers to new risks. Employers may now use analytics powered by artificial intelligence (“AI”) to identify which workers are most likely to develop future disabilities, raising the threat that these workers will face limited opportunities and discrimination before such action is clearly illegal.
Employers are accelerating predictive biometric surveillance, fueled by financial incentives like reduced health insurance costs and higher productivity, accommodations for older workers, and the ubiquity of predictive analytics in general. Current federal and state laws offer scant protection against discrimination based on predicted health risks rather than actual disabilities, creating a window of opportunity for employers and a danger zone for surveilled workers, especially older ones.
This Article critically examines the drivers and dangers of employer predictive health monitoring, reviews the gaps in existing regulation, and proposes specific reforms to mitigate the discrimination and privacy risks to workers. These legal reforms are urgently needed to protect workers’ privacy, security, and equal opportunity and to ensure that predictive health surveillance creates more benefits than risks.