Ai Predictor Theoretical Basis

AI predictors: theoretical basis (Concern 1)

Many technologically enhanced assessments use a wide variety of data scraped from applications, resumes, social media, emails, or the Internet, then run through hundreds of candidate ML algorithms. The substantive nature of the included variables and their linkages to job requirements are often unknown.

The problem

• No substantive rationale. Past-employer data scraped from resumes might show that employment at

Employers A, B, C predicts future performance while D, E, F do not — with no substantive post hoc explanation, even when all six are in the same business. Theology coursework might "predict" sales success. Voice or facial characteristics may have no obvious theory linking them to KSAOs or performance — justification is "inferred at best and unknown at worst."

• Proxy variables. Atheoretical predictors readily encode protected characteristics. In credit

scoring, ZIP code is a known proxy for race; AI using millions of correlations can base decisions on such hidden relationships. A predictor can "work" statistically while being a construct-irrelevant proxy.

• Interpretability ≠ relevance. Even when relationships are interpretable, they may have **little

practical or conceptual relevance** to the work performed (Braun & Kuljanin, 2015). Big data is often massive, messy, and missing.

The debate (represent both sides honestly)

I-O psychology has long debated whether predictors need a theoretical basis:

• "Theory matters." The traditional basis for including a measure is the extent to which it

reflects a KSAO necessary to perform the job, as determined by a job analysis. The Standards and Principles embed this in the very definition of validity: "the degree to which accumulated evidence and theory support specific interpretations of scores… entailed by the proposed uses" (Principles, p. 96; Standards, p. 225; emphasis added).

• "Prediction is enough." Others hold that if scores correlate with a relevant criterion

(performance, engagement, turnover), they're useful predictors and the rationale is merely "nice to know."

The article's resolution: if the only purpose is mechanical prediction, studying constructs and jobs is "merely a response to regulatory requirements." But if the purpose looks beyond simple prediction, understanding the predictive relationship yields improved measures, broader coverage of the performance domain, greater generalizability, and assurance that the system is sensible with respect to recruiting, training, diverse applicant pools, and change over time. Systematic research also surfaces additional variables and data sources that may predict, mediate, or explain work behavior (Rotolo & Church, 2015).

How adverse impact changes the calculus

• Without adverse impact, even a job-irrelevant predictor would not legally require the "job

related / business necessity" defense (Title VII) and is not unlawful per se. From this view, theory can look like an avoidable intellectual exercise.

• With adverse impact, job relatedness — which rests on relevance/theory established via job

analysis — becomes a legal requirement (see ai-selection-legal-landscape, ai-job-analysis-and-relevancy).

So the theoretical-basis question is partly scientific (do we want to understand prediction?) and partly contingent on adverse impact (do we legally have to?). The deeper issue: is selection research propelled by science, prioritizing understanding applicants' suitability through the lens of job requirements — or is it an atheoretical, purely empirical activity to maximize predicted outcomes?

Questions to ask (from the article)

• Are theoretical justifications necessary in employment testing?
• Is a technologically enhanced measure that predicts organizational outcomes sufficient, or does

one need to understand why that prediction occurs?

• Do theoretical justifications improve practice in employment testing?
• Do the considerations about theoretical justification **change when there is adverse impact versus

when there is not?**

Pitfalls

• Accepting "it predicts, so who cares why" when the use goes beyond one-shot mechanical prediction.
• Missing proxy variables that encode protected characteristics through hidden correlations.
• Assuming interpretability implies job relevance.
• Treating a serendipitous, unreplicated correlation as a justified predictor.

Checklist

• [ ] Each predictor's substantive link to a job-relevant KSAO examined (or its absence noted)
• [ ] Proxy-variable risk for protected characteristics assessed
• [ ] Purpose clarified: mechanical prediction only, or understanding/coverage/generalizability?
• [ ] Adverse-impact status checked and its legal implications applied
• [ ] Atheoretical/serendipitous relationships flagged for replication before use

See also

ai-job-analysis-and-relevancy · ai-selection-legal-landscape · ai-validity-evidence · work-analysis · criterion-related-validation (predictor choice, rationale) · ai-input-data-and-design-audit

Source: Tippins, Oswald & McPhail (2021), Concern: "Lack of a Theoretical Basis for Predictors."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: OpenMatter-Network
• Source: OpenMatter-Network/agent-io-skills
• License: MIT

Install and usage instructions live in the source repository linked above.