Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.¶
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations. Science, 366(6464), 447-453.
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Primes¶
- Garbage In, Garbage Out
- Investigation traces the failure not to the model but to the training target: the proxy for "future medical need" was "future medical-care spending," which diverged from need along access lines, so the input's validity floor-bound the output's fairness, and no model sophistication could fix it; the fix was to redefine the target — an input intervention.
This sourceA clinical risk algorithm under-served one population because its label (future medical-care spending) was a biased proxy for medical need; the fix was redefining the target, an input intervention no model sophistication could supply.
- Investigation traces the failure not to the model but to the training target: the proxy for "future medical need" was "future medical-care spending," which diverged from need along access lines, so the input's validity floor-bound the output's fairness, and no model sophistication could fix it; the fix was to redefine the target — an input intervention.
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