Report by CRS: “As automated tools for decisionmaking, including those using artificial intelligence (AI), have grown in popularity and sophistication, they raise questions about how their use intersects with federal antidiscrimination laws. Federal laws address various kinds of discrimination, including disparate treatment and disparate impact.
Disparate treatment generally occurs when a person makes a decision or classifies people on the basis of a protected trait, such as race, sex, religion, or another characteristic. Int’l Bhd. of Teamsters v. United States, 431 U.S. 324, 335 n.15 (1977).
Disparate impact occurs when a person makes a decision using neutral criteria, but the decision adversely and disproportionately affects members of a protected group, and either the decisionmaker cannot put forth a legally adequate justification for using the criteria, or an alternative exists that would serve the decisionmaker’s purposes with less of a disparate impact. Tex. Dep’t of Hous. & Cmty. Affs. v. Inclusive Cmtys. Project, Inc., 576 U.S. 519, 524–25 (2015).
How disparate treatment and disparate impact frameworks apply to decisionmaking via automated, algorithmic tools (including AI) is not always clear. Some situations involving algorithmic decisionmaking would clearly be disparate treatment under present law. For example, a person covered by antidiscrimination law who intentionally designs an algorithm to discriminate on the basis of a protected characteristic, or who selects an algorithm because of its discriminatory effect on a protected group, engages in disparate treatment. See Pers. Adm’r of Mass. v. Feeney, 442 U.S. 256, 279 (1979).
However, the law is unsettled on how to evaluate cases where no human consciously chooses to discriminate, and yet an automated system classifies people on the basis of protected characteristics. There is also substantial uncertainty over when an algorithmic system could be said to engage in disparate treatment via proxy—that is, classification based on a facially neutral trait that is closely correlated with a protected characteristic…(More)”.
