AI Selection Legal Landscape

AI selection: legal landscape

The legal/regulatory frame an AI selection tool must survive. This is decidedly U.S.-centric because the legal requirements are, but many of the concerns apply globally (validity as business justification exists everywhere). Not legal advice — consult qualified counsel.

Core principle: AI/ML selection tools are "tests" under the law. Uniform Guidelines Section 2B defines a test as any selection procedure used as the basis for an employment decision — which sweeps in games, video interviews, facial/voice scoring, resume screeners, and big-data models. They are governed by the same rules as any other test.

The federal framework (U.S.)

Uniform Guidelines on Employee Selection Procedures (EEOC, CSC, DoL, DoJ, 1978)

• Broadened "test" to any selection procedure used for an employment decision (Sec. 2B; the 1979 Q&A further expanded it to application forms, interviews, training/probationary performance, etc.).
• Adopted 1978, with very few changes and none since the 1981 Q&A (the OFCCP's 2019 FAQs are not part of the Guidelines). Theoretically and practically dated — yet they remain the controlling administrative rules for Title VII litigation and are deeply embedded in case law. They must still be considered when evaluating any procedure that results in adverse impact.
• Require a job analysis whenever conducting a criterion-related validation study, and specify the documentation expected in technical reports.

Title VII, Civil Rights Act of 1964 — disparate impact

A disparate-impact violation is established when a complaining party shows a practice causes disparate (adverse) impact on the basis of race, color, religion, sex, or national origin and the respondent fails to demonstrate the practice is job related for the position and consistent with business necessity (Sec. 2000e-2(k)(1)(A)(i)). So:

• Adverse impact shifts the burden to the employer to show job relatedness / business necessity — which in practice means validity evidence (see ai-validity-evidence).
• Differential treatment (treating class members differently — e.g., awarding bonus points to a group, or modeling class membership) is a distinct, separate legal problem.

OFCCP (2019 FAQ)

"Irrespective of the level of technical sophistication involved, OFCCP analyzes all selection devices for adverse impact." If a contractor's AI-based procedure has adverse impact, the contractor must validate it using an appropriate validation strategy. AI buys no exemption.

Search for less-adverse alternatives

U.S. employers are obligated to consider alternative selection procedures with substantially equal or greater validity and less adverse impact (Uniform Guidelines §3B). This makes comparative data important — an AI tool should be compared against alternatives, including traditional measures whose meta-analytic validity provides a reasonable baseline the AI must beat.

Case law — job analysis in content validation

Guardians Association of NYC Police Dept. v. Civil Service Commission of NYC (2d Cir. 1980) and related cases establish that job analysis is central in content-validation disputes and should be systematic and accurate regardless of the methodology used. (See ai-job-analysis-and-relevancy.)

State law (proliferating)

• Illinois Artificial Intelligence Video Interview Act (passed May 29, 2019; effective Jan. 1, 2020): employers using AI to evaluate video interviews must (a) notify applicants in writing that AI may be used and what characteristics it evaluates, (b) provide information on how the technology works and what characteristics it uses, and (c) obtain written consent before the interview. They may not share the video except with those with expertise to evaluate it, and must destroy the video within 30 days of a request.
• Other state legislatures are weighing in; legislation and policy on the nature, transparency, and privacy of applicant data will continue to emerge and evolve. Re-check current jurisdictional law.

Professional (not legal) — the APA Ethics Code

SIOP members subscribe to the APA Ethics Code, which governs the treatment of candidates and what psychologists say about tools — an obligation independent of, and additional to, the law. See ai-selection-ethics.

The "no adverse impact ≠ valid" rule

A crucial argument to deploy: reduced or no adverse impact does not, by itself, justify use. A random-number generator can winnow a large applicant pool quickly without producing adverse impact — yet it lacks the reliability, validity, and utility an organization needs to identify capable candidates and achieve an acceptable return. It is incumbent on developers (and users) to provide sufficient evidence that AI selection tools meet these requirements — not merely to show they don't create adverse impact. Vendors often tout "reduced adverse impact" while the empirical validity evidence is unavailable, making the relevant comparison impossible.

Pitfalls

• Treating an AI tool as exempt from test-validation law because it's "just an algorithm."
• Relying on "no/low adverse impact" as if it established validity or utility.
• Forgetting the duty to consider less-adverse, equally valid alternatives.
• Overlooking state-specific notice/consent/destruction requirements (e.g., Illinois).
• Assuming the OFCCP 2019 FAQs are part of the Uniform Guidelines (they are not).
• Conflating disparate impact with disparate (differential) treatment.

Checklist

• Tool classified as a "test" under UGESP §2B
• Adverse-impact exposure assessed; validity-defense obligation understood
• Job-relatedness/business-necessity evidence path identified (→ ai-validity-evidence)
• Less-adverse-alternative comparison considered (§3B)
• Job-analysis adequacy checked (Guardians) (→ ai-job-analysis-and-relevancy)
• State-law notice/consent/destruction requirements checked (e.g., Illinois AIVI Act)
• "No adverse impact ≠ valid" argument applied to vendor claims
• APA Ethics Code obligations noted (→ ai-selection-ethics)
• Counsel engaged for jurisdiction-specific questions

See also

ai-validity-evidence · ai-job-analysis-and-relevancy · ai-candidate-data-control (consent) · ai-selection-ethics · fairness-and-bias-analysis · criterion-related-validation (alternatives, validation)

Source: Tippins, Oswald & McPhail (2021), "New Forms of Assessment" (legal framing), "Disadvantages," "Purpose," and the legal threads woven through the concerns.