Editing AI for Human Resources (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
**The promise**: Structured, data-driven hiring should reduce the bias of unstructured, impression-based interviewing. Humans are notoriously inconsistent and susceptible to affinity bias (favoring candidates like themselves), primacy/recency effects, and stereotype threat. Algorithmic hiring could standardize evaluation on job-relevant criteria.

**The reality**: The Amazon resume screening scandal (2018) exposed the core problem. Amazon trained a hiring ML model on 10 years of successful employee resumes — predominantly male engineers. The model learned to penalize resumes containing words like "women's" (as in "women's chess club") and downgraded graduates of all-female universities. The system had learned historical discrimination patterns from training data.

**What's legal in hiring AI**: The EEOC requires that employment tests (including AI assessments) show validity — they must predict actual job performance — and not produce disparate impact against protected classes. Video interview AI like HireVue has faced scrutiny; Illinois requires informed consent for AI video analysis. The EU AI Act classifies employment AI as high-risk, requiring conformity assessments.

**Attrition prediction**: ML models trained on HR data (tenure, performance ratings, compensation, promotion history, manager changes) can predict which employees are likely to leave with AUC 0.7–0.8. This enables proactive retention interventions. Ethical concerns: does the organization use this information to help employees or to avoid promoting them (to reduce attrition risk)?

**Skills-based HR**: Moving from credential-based hiring (degree requirements) to skills-based hiring using NLP to extract and match skills from resumes to job requirements. Platforms like LinkedIn, Eightfold, and Beamery build skills taxonomies and use them to match candidates to roles across the entire organization.
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