AI Validity Evidence

AI validity evidence (Concern 5)

Among I-O psychologists there is no doubt about the importance of validity. Evidence of validity is necessary to demonstrate a selection procedure's effectiveness, and it is a legal requirement when adverse impact is observed. For business and legal-defensibility reasons, validity is a sine qua non of selection research — including for AI tools.

This is the AI-specific framing; for how to gather validity evidence, use criterion-related-validation, content-based-validation, and generalizing-validity-evidence.

AI tools are "tests" — so they need validity

AI-based predictors, game-oriented tools, and evaluations of facial features and voice qualities used to decide whom to hire are tests under both the law and professional guidelines (UGESP §2B: a test is any selection procedure used for an employment decision). Therefore:

• In the U.S., their use must be supported by validity evidence if adverse impact results.
• In all countries, validity evidence provides the business justification for use.

Which validity strategy — and the AI wrinkle

• Criterion-related — a statistical relationship between scores and a relevant criterion (correlation/regression). Report strength via size, confidence interval, and significance; regression should indicate imprecision (CIs, bootstrapping, cross-validation). Traditional selection rarely models nonlinearities (linearity affords interpretability and needs less power); ML does model and cross-validate nonlinearities, but interpretation is difficult and power may or may not be an issue depending on the number of variables and cases.
• Content — SMEs relate the content of the test to job requirements. Because of the nature of AI-based tests, content validation evidence is often not used at all, or only to supplement a criterion-related study. (When content can't carry the argument, criterion evidence and job analysis must.)

When overall model fit is the only handle

Some ML methods are based on regression (logistic, lasso, ridge, elastic net) and yield coefficients — a "demonstration of a relationship." But complex models (random forests, neural nets) or clustering approaches don't express relationships as apparent, direct coefficients. There, overall model fit (analogous to R² in regression) may be the only basis for establishing the predictor–criterion relationship — which makes job analysis all the more important to ensure job-relevant predictors and criteria are the ones in the model, even when their weighting is opaque.

Comparative data and the alternatives duty

Because U.S. employers are obligated to search for alternative procedures with equal or greater validity and less adverse impact (UGESP §3B), comparative data are particularly important. The empirical, meta-analytic history of validities in I-O psychology can serve as a reasonable baseline the ML model should have to beat — but vendor claims of "higher validity / lower adverse impact" are frequently unaccompanied by the evidence needed to make the comparison.

Fundamental requirements and documentation

A key open question: should users of algorithm-based procedures be required to provide specific conceptual and empirical evidence of predictor–criterion relationships, in addition to overall model metrics? The Uniform Guidelines, Principles, and Standards all enumerate the components a technical report should document — yet critical information is often omitted, and the problem is acute for AI because so many decisions are made in selecting and tuning the model. Documentation should let an external party understand, evaluate, and reproduce the work. Ask:

• How were data obtained, prepared, cleaned, transformed, and combined? What approach handled missing data?
• With text mining / written responses, were effects of vocabulary knowledge or verbal fluency controlled where the text wasn't meant to measure verbal ability?
• How was conflicting applicant data weighted, discarded, or cleaned?
• Was one algorithmic approach taken or several, and could an external party reproduce the analysis?
• Were algorithm choices a priori or post hoc? Were predictions combined across algorithms?
• Was there post hoc filtering — inspecting results to pick the algorithm that "looked good" (a suspect practice)?

Questions to ask (from the article)

• What is sufficient evidence of validity when ML models are used?
• What details of the AI research must be documented?
• What details of the AI research must be shared with users?

Pitfalls

• Treating "no adverse impact" as a substitute for validity (see ai-selection-legal-landscape).
• Accepting overall model fit (R²/AUC) without job-analytic grounding of the predictors/criterion.
• Accepting "more valid, less adverse impact" claims without the comparative evidence.
• Tolerating post hoc algorithm selection / filtering without cross-validation safeguards.
• Thin documentation that no external party could reproduce.

Checklist

• Tool treated as a "test" requiring validity (esp. if adverse impact)
• Validity strategy chosen (criterion-related / content / generalization) and justified for AI
• If only overall model fit is available, predictor/criterion job-relevance secured via job analysis
• Imprecision reported (CIs, bootstrapping, cross-validation)
• Comparative data vs. traditional/meta-analytic baselines and less-adverse alternatives provided
• Data prep, missing-data, weighting, and algorithm-selection decisions documented and reproducible
• Post hoc filtering ruled out or controlled; evidence shared with users

See also

criterion-related-validation · content-based-validation · generalizing-validity-evidence · technical-validation-report · ai-selection-tech-data-algorithms (why a technology is not "universally valid") · ai-ml-methodology-evaluation (model-fit metrics) · ai-job-analysis-and-relevancy · ai-selection-legal-landscape · ai-model-outputs-audit

Source: Tippins, Oswald & McPhail (2021), Concern: "Validity."