ai-selection-legal-ethical

Use when considering how candidates react to an AI/ML selection tool and what is communicated to candidates and stakeholders about it — Concerns 9-10 of Tippins, Oswald & McPhail (2021). Covers applicant reactions and their tenuous link to behavior, the faking-vs-training question for video interviews, pitfalls in reaction metrics, and what information can/should be shared with unsuccessful applicants and other stakeholders. Triggers: "candidate reactions to AI hiring", "applicant perceptions video interview", "faking vs training interview", "what to tell rejected candidates", "explain AI hiring decision", "what to share with stakeholders about selection".

Use when an AI/ML selection tool uses data the candidate does not control or did not knowingly provide — scraped social-media/Internet data, or incidental data like facial micro-expressions, voice, and appearance — Concern 8 of Tippins, Oswald & McPhail (2021). Covers the loss of applicant control, job-irrelevance and "is it fair," reputation-scrubbing services and adverse impact, the absence of a clear legal/ethical rule, informed consent (Illinois AIVI Act), and the range of policy approaches. Triggers: "scraped social media hiring", "data outside applicant control", "facial appearance in hiring", "is it fair to use this data", "informed consent for AI hiring data", "online reputation scrubbing".

Use when an AI/ML selection tool uses "dynamic" models or norms that update frequently (sometimes after every administration), or when deciding how often to revalidate and update norms — Concern 7 of Tippins, Oswald & McPhail (2021). Covers the real-change-vs-instability dilemma, technical-report/documentation updates, score adjustments and grandparenting, disparate-treatment risk from candidates evaluated on different variables, applicant-pool shifts affecting validity/range restriction, and revalidation cadence. Triggers: "dynamic models hiring", "algorithm updates after every administration", "how often revalidate AI", "norms updating", "grandparenting test scores", "candidates scored on different variables", "continuous validation".

Use when framing the profession-level response to AI selection tools, or orienting a project to the governing standards — the "Call to Action" of Tippins, Oswald & McPhail (2021). Covers the Principles and Standards as the two guiding documents, the argument that SIOP should develop interpretive guidance APPLYING the Principles to technologically enhanced assessments (not rewrite them), the need for interdisciplinary collaboration, and the warning against letting practice reach "escape velocity" from scientific, legal, and ethical moorings. Triggers: "how should the profession respond to AI hiring", "extend the Principles to AI", "interpretive guidance for AI assessments", "what standards govern AI selection", "I-O psychologists role in AI hiring", "call to action AI selection".

Use when checking whether an AI/ML selection tool is grounded in an adequate analysis of work and is demonstrably job-related — Concerns 2-3 of Tippins, Oswald & McPhail (2021). Covers whether a job analysis is necessary (even with a strong criterion-related relationship), acceptable forms and rigor, O*NET and competency-model limits, collecting task importance ratings, SME-judgment agreement, and the legal meaning of job relatedness. Triggers: "does the AI tool need a job analysis", "is this algorithm job-related", "competency model vs job analysis for AI", "O*NET as job analysis", "job relevancy of scraped predictors", "Guardians job analysis".