ai-selection-legal-ethical

Plugin ai-selection-legal-ethical from marketplace io-skills in OpenMatter-Network/agent-io-skills. Commands read from the repo's manifest — 1 month ago.

Skills (12)

• ai-applicant-reactions-and-communications

  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 intervie

• ai-candidate-data-control

  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,

• ai-dynamic-models-and-revalidation

  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/do

• ai-extending-professional-standards

  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 gui

• ai-job-analysis-and-relevancy

  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

• ai-ml-methodology-evaluation

  Use when evaluating whether the machine-learning methodology behind a selection tool is appropriate and interpretable — Concern 4 of Tippins, Oswald & McPhail (2021). Covers ML interpretability and the "black box," explainable AI (XAI), evaluation metrics (MSE, confusion matrix, ROC/AUC), the high v

• ai-predictor-theoretical-basis

  Use when an AI/ML selection tool uses predictors with no clear theoretical or job-analytic rationale — scraped data (resumes, social media, emails, the Internet), voice/facial features, or opaque big-data correlations. Covers the debate over whether predictors need a theoretical basis, proxy-variabl

• ai-reliability

  Use when evaluating the reliability (score consistency/stability) of an AI/ML selection tool — Concern 6 of Tippins, Oswald & McPhail (2021). Covers reliability as an absolute requirement, what stability means for AI scores, evidence that machine scoring can be as or more reliable than human scoring

• ai-selection-ethics

  Use when evaluating the professional-ethics obligations around an AI/ML personnel selection tool under the APA Ethics Code — Concern 11 of Tippins, Oswald & McPhail (2021). Covers Ethics Code Section 9 (9.01 Bases for Assessments, 9.02 Use of Assessments, 9.03 Informed Consent), the difference in co

• ai-selection-legal-landscape

  Use when assessing the legal and regulatory exposure of an AI-based / technologically enhanced personnel selection tool (primarily U.S., with global notes). Covers the Uniform Guidelines on Employee Selection Procedures, Title VII disparate impact and the job-relatedness/business-necessity defense,

• ai-selection-tech-data-algorithms

  Use FIRST when evaluating, classifying, or comparing any AI-based or technologically enhanced personnel selection tool — to separate the three independent things it combines: technologies, data, and algorithms (Tippins, Oswald & McPhail, 2021). Establishes that a technology is never "universally val

• ai-validity-evidence

  Use when determining what validity evidence an AI/ML selection tool needs and whether it has it — Concern 5 of Tippins, Oswald & McPhail (2021). Covers validity as the legal/business sine qua non when adverse impact exists, AI tools as "tests" requiring validity, criterion-related vs. content strate