Daniel Schwarcz (University of Minnesota Law School) has posted “Health-Based Proxy Discrimination, Artificial Intelligence, and Big Data” (Houston Journal of Health Law and Policy, 2021) on SSRN. Here is the abstract:
Insurers and employers often have financial incentives to discriminate against people who are relatively likely to experience future healthcare costs. Numerous federal and states laws nonetheless seek to restrict such health-based discrimination. Examples include the Pregnancy Discrimination Act (PDA), the Americans with Disabilities Act (ADA), the Age Discrimination in Employment Act (ADEA), and the Genetic Information Non-Discrimination Act (GINA). But this Essay argues that these laws are incapable of reliably preventing health-based discrimination when employers or insurers rely on machine-learning AIs to inform their decision-making. At bottom, this is because machine-learning AIs are inherently structured to identify and rely upon proxies for traits that directly predict whatever “target variable” they are programmed to maximize. Because the future health status of employees and insureds is in fact directly predictive of innumerable facially neutral goals for employers and insurers respectively, machine-learning AIs will tend to produce similar results as intentional discrimination based on health-related factors. Although laws like the Affordable Care Act (ACA) can avoid this outcome by prohibiting all forms of discrimination that are not pre-approved, this approach is not broadly applicable. Complicating the issue even further, virtually all technical strategies for developing “fair algorithms” are not workable when it comes to health-based proxy discrimination, because health information is generally private and hence cannot be used to correct unwanted biases. The Essay nonetheless closes by suggesting a new strategy for combatting health-based proxy discrimination by AI: limiting firms’ capacity to program their AIs using target variables that have a strong possible link to health-related factors.
Recommended.
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