On the Inequity of Predicting A While Hoping for B
AEA Papers and Proceedings
vol. 111,
May 2021
(pp. 37-42)
Abstract
Algorithms trained to predict mismeasured proxy variables can reproduce and scale up racial bias. This mechanism of algorithmic bias is distinct from others in the literature and harder to detect. We show this using examples from health care, but the forces we consider apply to a range of other important social sectors.Citation
Mullainathan, Sendhil, and Ziad Obermeyer. 2021. "On the Inequity of Predicting A While Hoping for B." AEA Papers and Proceedings, 111: 37-42. DOI: 10.1257/pandp.20211078Additional Materials
JEL Classification
- D63 Equity, Justice, Inequality, and Other Normative Criteria and Measurement
- J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
- I14 Health and Inequality