Evaluating AI-based selection tools against scientific, legal, and ethical standards (Uniform Guidelines, Title VII, APA Ethics Code), per Tippins, Oswald & McPhail (2021).
12 skills
Validating and using personnel selection procedures, per the SIOP/APA Principles (5th ed., 2018): work analysis, criterion/content/internal-structure validity, generalization, fairness, scoring decisions, reporting, administration, accommodations.
Plugin personnel-selection from marketplace io-skills in OpenMatter-Network/agent-io-skills. Commands read from the repo's manifest — 1 month ago.
administration-documentationUse when preparing the administration documentation / manual for an operational selection procedure — the materials administrators and users need to administer, score, interpret, secure, and communicate the procedure consistently. Covers administrator qualifications, the testing environment, scoring
candidate-accommodationsUse when assessing candidates with disabilities or from different linguistic/ cultural backgrounds — deciding and documenting selection-procedure accommodations vs. modifications, preserving score comparability and construct measurement, and handling translation/adaptation. Covers the accommodation-
content-based-validationUse when building validity evidence by linking selection-procedure content to the work domain — demonstrating that the procedure samples important work behaviors, activities, and/or worker KSAOs defined by an analysis of work. Covers defining the content domain, SME qualifications and linkage judgme
criterion-related-validationUse when designing, conducting, or evaluating a criterion-related validity study — demonstrating an empirical relationship between selection-procedure (predictor) scores and work-relevant criteria. Covers predictive vs. concurrent designs, criterion development (relevance/contamination/deficiency/ r
fairness-and-bias-analysisUse when evaluating fairness and bias of a selection procedure — distinguishing the several meanings of "fairness," testing for predictive bias (differential prediction via moderated regression), and examining measurement bias (DIF, item sensitivity review). Covers what subgroup-mean differences do
generalizing-validity-evidenceUse when justifying a selection procedure with validity evidence gathered elsewhere instead of (or alongside) a local study — via transportability, synthetic/job-component validity, or meta-analytic validity generalization (VG). Covers when each applies, the work-analysis link required, moderators,
internal-structure-validationUse when examining the internal structure of a selection procedure as supporting validity evidence — the relationships among items/components and how they conform to the intended conceptual framework (unidimensional vs. multidimensional). Covers dimensionality, factor-analytic models, when coefficie
selection-decisions-and-scoringUse when deciding how to combine selection procedures and turn scores into decisions — compensatory vs. multiple-hurdle models, cutoff scores, banding, rank-order/top-down selection, norms, and communicating effectiveness via expectancy charts and utility. Covers the validity/diversity tradeoffs and
technical-validation-reportUse when writing or reviewing the technical validation report that documents a selection-procedure validation effort — the record that lets another competent professional understand, evaluate, replicate, and draw independent conclusions about the work. Covers every required section and a reusable ou
validation-planningUse when planning or scoping a personnel-selection validation effort BEFORE data collection — to define the organization's needs, objectives, and constraints, specify the proposed uses and inferences, decide whether existing evidence suffices or new evidence is needed, and choose a validation strate
work-analysisUse when analyzing work to support a selection effort — to identify worker requirements (KSAOs/competencies), define the work content domain, or develop job-relevant criteria. Covers choosing methods, level of detail, SME sampling, competency modeling, and documenting results. Triggers: "job analysi
Evaluating AI-based selection tools against scientific, legal, and ethical standards (Uniform Guidelines, Title VII, APA Ethics Code), per Tippins, Oswald & McPhail (2021).
12 skills
Auditing AI/ML personnel assessments for fairness, bias, validity, and utility, using the 12-component psychological-audit framework of Landers & Behrend (2023), 'Auditing the AI Auditors.'
8 skills
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