{"database": "openregs", "table": "crs_reports", "rows": [["R49490", "Algorithmic Discrimination: Legal Principles", "2026-10-20T04:00:00Z", "2026-10-22T07:37:56Z", "Active", "Reports", "Abigail A. Graber", "Artificial Intelligence", "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\u2019l 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\u2019s purposes with less of a disparate impact. Tex. Dep\u2019t of Hous. & Cmty. Affs. v. Inclusive Cmtys. Project, Inc., 576 U.S. 519, 524\u201325 (2015).\nHow 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\u2019r 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\u2014that is, classification based on a facially neutral trait that is closely correlated with a protected characteristic. The law is not clear on whether proxy discrimination always requires human intent to use a proxy to discriminate, or whether some characteristics are so closely related to protected traits that they are inherent proxies. It is also not clear how one could prove that certain AI systems are engaging in proxy discrimination if there is no way to know how some systems arrive at their conclusions. How laws handle such situations, and how difficult it is for plaintiffs to prove their case, may also depend on the causation and intent standards specific to each law.\nMany unanswered questions persist regarding when and how the use of algorithms can give rise to disparate impact claims, as well. Using algorithmic tools can result in disparate impacts in several ways. An algorithm could use decision rules or targeted outcomes that proxy for protected characteristics or that are more or less favorable to certain groups. Algorithms could rely on criteria that are less predictive for certain groups than others, or fail to consider criteria that would make them more predictive for particular groups. AI systems may be trained on data that are affected by past discrimination, or that are over- or under-representative of particular groups, potentially leading to biased or less accurate outcomes for members of those groups. See Solon Barocas & Andrew D. Selbst, Big Data\u2019s Disparate Impact, 104 Cal. L. Rev. 671, 677\u201394 (2016).\nThere are already gray areas in legally significant elements of disparate impact doctrine. These uncertainties make it hard to predict how courts will respond when algorithmic decisionmaking is added to the mix. Courts may have to decide, for example, whether algorithmic systems should be analyzed as a whole or whether plaintiffs can, or must, challenge particular elements of how a system works. They may have to determine whether a decisionmaker can rely on the fact that an algorithm\u2019s predictions are accurate, or whether the decisionmaker has to posit or prove how the algorithm works or why it is accurate. Courts may also need to decide how much of a premium decisionmakers may place on cost savings or efficiency when justifying the use of an algorithm that causes a disparate impact. To evaluate plaintiffs\u2019 proposed less discriminatory alternatives, courts may need to grapple with whether plaintiffs must put forward alternatives that are as efficient and cost-effective as a challenged algorithm and what standards govern showing that an alternative\u2014including an alternative algorithm\u2014is available.\nCongress has many options if it chooses to regulate algorithmic decisionmaking to prevent discrimination or bias. It may consider amending current antidiscrimination laws to better take into account the unique features of algorithmic decisionmaking, or it can pass laws specifically targeted at potential algorithmic bias. It may prohibit discrimination or require other, affirmative conduct by those that develop or deploy algorithmic systems, such as greater transparency, bias audits, or guaranteed human participation in certain decisions. Some potential pathways to mitigating the risks of algorithmic discrimination may raise constitutional concerns under the Takings Clause, First Amendment, and Equal Protection Clause. Congress may also consider how to balance the risks of discrimination with the risks of regulation, which could stifle the growth of automated tools, including those that may advance less discriminatory decisionmaking.", "https://www.congress.gov/crs_external_products/R/PDF/R49490/R49490.1.pdf", "https://www.congress.gov/crs_external_products/R/HTML/R49490.html"]], "columns": ["id", "title", "publish_date", "update_date", "status", "content_type", "authors", "topics", "summary", "pdf_url", "html_url"], "primary_keys": ["id"], "primary_key_values": ["R49490"], "units": {}, "query_ms": 0.17996784299612045, "source": "U.S. government sources, plus a few identifier registries; each is listed on the methodology page", "source_url": "https://datadawn.org/methodology#data", "license": "Public Domain (U.S. Government data)", "license_url": "https://www.regulations.gov/faq"}