People Management Prover MCP Connector for Claude
A+A hiring plan listed 'culture fit' as the primary criterion. That's not a criterion — that's a bias proxy. People Management Prover forces job-related criteria, adverse impact analysis, and validated assessment methods grounded in I-O psychology.
AI agents produce HR recommendations that sound professional but fail under legal scrutiny. They evaluate candidates on 'culture fit' — a term that correlates with interviewer similarity bias (Rivera, 2012). They recommend hiring decisions without structured criteria. They give feedback like 'needs improvement' without specifying what to improve or how.
The Problem It Solves
AI-generated HR reasoning fails for five specific reasons:
- Criteria absence — Evaluating candidates without job-related, measurable criteria defined before assessment. 'Looking for the best candidate' is a wish, not a selection framework.
- Bias blindness — Ignoring adverse impact analysis. 'We hire on merit' without calculating selection rates by protected group is an assumption, not an audit. Bohnet (2016) demonstrated standardized criteria reduce gender bias 25-46% — but only with structured scoring.
- Legal ignorance — Jurisdiction-blind recommendations. Title VII applies at 15+ employees in the US. GDPR Art. 22 governs automated decisions in the EU. CLT governs all employment in Brazil. The AI doesn't know which law applies — and doesn't ask.
- Assessment theater — Using unvalidated methods. Schmidt & Hunter (1998) show structured interviews (r=0.51) dramatically outperform unstructured (r=0.38) and gut feeling (near-zero validity). 'We use interviews' is a category, not a method.
- Feedback vacuum — Hattie's meta-analysis shows praise (d=0.09) has near-zero impact on performance. Effective feedback is behavioral, criteria-referenced, and developmental — not 'good job.'
Key Benefits
- Forces job-related criteria — Every evaluation criterion must trace to an essential job function, required KSA, behavioral indicator, and anchored scoring rubric.
- Audits for adverse impact — Selection rates by protected group, 4/5ths rule analysis, pipeline-stage examination, and documented mitigations.
- Verifies legal compliance — Identifies the governing jurisdiction, applicable statutes, protected classes, and prohibited inquiry areas before any recommendation.
- Demands validated assessment — Predictive validity coefficients, structured scoring, and inter-rater reliability. No more gut feeling decisions.
- Requires developmental feedback — Behavioral, criteria-referenced, forward-looking guidance per Hattie & Timperley's feed up/feed back/feed forward model.
Framework Coverage
- Schmidt & Hunter (1998) — Selection method validity meta-analysis
- Bohnet (2016) — Gender-bias reduction through structured evaluation
- Hattie & Timperley (2007) — Feedback effect sizes
- EEOC Uniform Guidelines — Adverse impact and selection procedures
- Title VII / ADA / ADEA — US anti-discrimination
- GDPR Art. 22 / EU AI Act — Automated decision-making
- CLT / Lei 9.029 — Brazil labor law
- Equality Act 2010 — UK anti-discrimination
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