# Hiring AI Fairness Review

> Use this skill when designing, reviewing, or auditing recruiting, resume screening, interview, assessment, ranking, HR analytics, job matching, or hiring AI workflows for fairness, adverse-impact risk, privacy, accessibility, explainability, and required human decision-making.

- Skill: `srednoff888-art/hiring-ai-fairness-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add srednoff888-art/hiring-ai-fairness-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/srednoff888-art/hiring-ai-fairness-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: srednoff888-art (https://skillmd.com/u/srednoff888-art)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/srednoff888-art/hiring-ai-fairness-review

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# Hiring AI Fairness Review

Treat hiring and recruiting automation as high-impact decision support that needs human ownership, evidence, and anti-discrimination controls.

## Workflow

1. Map the hiring workflow: sourcing, outreach, screening, assessment, ranking, interview support, offer, rejection, or HR analytics.
2. Identify what the AI influences: candidate visibility, score, recommendation, interview question, rejection reason, or human decision.
3. Inventory data fields and proxies: protected-class indicators, education, gaps, location, salary history, disability-related signals, photos, names, age proxies, and scraped social data.
4. Check legal/policy context with official sources for the relevant jurisdiction before making compliance claims.
5. Require validation evidence: job-related criteria, accessibility, bias testing, adverse-impact monitoring, explainability, candidate notice, appeal/review path, and data retention limits.
6. Keep humans accountable: AI may summarize or draft, but qualified people own screening criteria and final employment decisions.
7. Review candidate communications for transparency, non-deceptive claims, and no unsupported guarantees.
8. Produce findings with affected stage, risk, evidence gap, mitigation, and owner.

## Checklist

- Use structured, job-related rubrics instead of vague culture-fit or personality judgments.
- Separate protected data used for auditing from data used for ranking.
- Test for disparate outcomes before rollout and after material model, prompt, vendor, or data changes.
- Provide accommodations and non-AI alternatives where required or appropriate.
- Minimize personal data and avoid unnecessary social-media or sensitive-data enrichment.
- Preserve audit trails for criteria, model configuration, prompts, reviewer decisions, and candidate appeals.

## Guardrails

- Do not recommend hiring, rejecting, ranking, or compensating a person based solely on an AI output.
- Do not infer protected characteristics or use sensitive health, disability, race, religion, pregnancy, age, or union-related data for selection.
- Do not claim legal compliance without jurisdiction-specific review by qualified counsel or HR compliance owner.
- Do not scrape or process candidate data beyond the user's approved systems and stated purpose.