Audit hiring pipelines, job ads, screening criteria, and AI-recruiting tools for bias and legal/compliance risk (disparate impact, EU AI Act high-risk rules, PDPL/GDPR candidate data). Use when the user asks to check fairness of a hiring process, audit an AI screening tool, review adverse-impact metrics, or make recruitment compliant.
npx skillsify add hiring-bias-auditorL'IA de recrutement est classée haut risque par l'AI Act européen, et plusieurs juridictions imposent désormais des audits de biais des outils de recrutement automatisés. Cette skill audite quatre couches — le langage des annonces, les critères de présélection et leurs proxys, les statistiques de résultats avec la règle des quatre cinquièmes par étape de l'entonnoir, et l'outil d'IA lui-même : explicabilité, provenance des données d'entraînement, humain dans la boucle et information des candidats. Elle relie les contrôles aux régimes UE, PDPL saoudien et américain en refusant d'affirmer des exigences légales sans vérifier les textes en vigueur — les affirmations de conformité sont vérifiées ou transmises au juriste, jamais présumées.
---
name: hiring-bias-auditor
description: "Audit hiring pipelines, job ads, screening criteria, and AI-recruiting tools for bias and legal/compliance risk (disparate impact, EU AI Act high-risk rules, PDPL/GDPR candidate data). Use when the user asks to check fairness of a hiring process, audit an AI screening tool, review adverse-impact metrics, or make recruitment compliant."
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# Hiring Bias & Compliance Auditor
## Purpose
Recruitment AI is classified as HIGH-RISK under the EU AI Act; several jurisdictions require bias audits of automated employment decision tools (e.g. NYC Local Law 144). This skill audits processes and tools for fairness and compliance.
## Audit scope (4 layers)
1. **Language layer** — JDs and outreach: gendered/age-coded words, unnecessary requirements creating disparate impact, degree inflation.
2. **Criteria layer** — screening/interview criteria: is every criterion job-related and validated? Flag proxies (school prestige, employer brand, employment gaps, accent, address/commute).
3. **Outcome layer (statistics)** — selection rates per stage by group (where lawful to collect): apply the **four-fifths rule** (any group's selection rate < 80% of the highest group's rate = potential adverse impact → investigate). Funnel-stage analysis locates WHERE loss happens: screen, interview, offer, acceptance.
4. **Tool layer (AI/automation)** — for any AI screening/matching tool:
- Can every score be explained with evidence? (black-box ranking = reject)
- Training-data provenance & historical bias
- Human-in-the-loop: meaningful human decision point? (fully automated rejection = high risk)
- Candidate disclosure & consent; accommodation process
- Logging/audit trail retention
## Compliance quick map (verify current law per jurisdiction — do not assert without checking)
- EU: AI Act high-risk obligations (risk mgmt, data governance, human oversight, logging); GDPR (lawful basis, retention limits, automated-decision Art. 22)
- Saudi: PDPL — candidate consent, purpose limitation, retention schedule, breach notification
- US: EEOC / Uniform Guidelines, state pay-transparency & salary-history bans, NYC AEDT audit
- Retention: define candidate-data retention (commonly 6–24 months post-process) + deletion workflow
## Deliverables
- Bias audit report: findings by layer, severity (blocker/major/minor), evidence, remediation
- Four-fifths analysis table per funnel stage (when data provided)
- AI-tool compliance checklist (pass/fail per control)
- Remediation plan with owners and deadlines
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0 installations · v1.0.0
Build employer brand and Employee Value Proposition: EVP definition, careers-page content, recruitment marketing, review-site management, and talent-audience campaigns. Use when the user asks to improve employer brand, write a careers page, define an EVP, respond to Glassdoor/Indeed reviews, or run recruitment marketing.
0 installations · v1.0.0
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0 installations · v1.0.0