Communications of the ACM
Addressing Algorithmic Discrimination
Credit: Andrij Borys Associates, Shutterstock
It should no longer be a surprise that algorithms can discriminate. A criminal risk-assessment algorithm is far more likely to erroneously predict a Black defendant will commit a crime in the future than a white defendant.2 Ad-targeting algorithms promote job opportunities to race- and gender-skewed audiences, showing secretary and supermarket job ads to far more women than men.1 A hospital's resource-allocation algorithm favored white over Black patients with the same level of medical need.5 The list goes on. Algorithmic discrimination is particularly troubling when it affects consequential social decisions, such as who gets released from jail, or has access to a loan or health care.
Employment is a prime example. Employers are increasingly relying on algorithmic tools to recruit, screen, and select job applicants by making predictions about which candidates will be good employees. Some algorithms rely on information provided by applicants, such as résumés or responses to questionnaires. Others engage them in video games, using data about how they respond in different situations to infer personality traits. Another approach harvests information from online interactions, such as analyzing video interviews for voice patterns and facial expressions. These strategies are aimed at helping employers identify the most promising candidates, but may also reproduce or reinforce existing biases against disadvantaged groups such as women and workers of color.
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