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- Title
DATA-DRIVEN DISCRIMINATION AT WORK.
- Authors
KIM, PAULINE T.
- Abstract
A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although data algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Algorithms built on inaccurate, biased, or unrepresentative data can produce outcomes biased along lines of race, sex, or other protected characteristics. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they may obscure the basis on which employment decisions are made; and they may further exacerbate inequality because error detection is limited and feedback effects compound the bias. Given these risks, I argue for a legal response to classification bias--a term that describes the use of classification schemes, such as data algorithms, to sort or score workers in ways that worsen inequality or disadvantage along the lines of race, sex, or other protected characteristics. Addressing classification bias requires fundamentally rethinking antidiscrimination doctrine. When decision-making algorithms produce biased outcomes, they may seem to resemble familiar disparate
- Subjects
EQUALITY in the workplace; RIGHT of privacy; DATA mining; DATA protection; RICCI v. DeStefano
- Publication
William & Mary Law Review, 2017, Vol 58, Issue 3, p857
- ISSN
0043-5589
- Publication type
Article