AI Interviews in 2025 — A Practical Fairness & Compliance Checklist

AI-assisted interviews can improve consistency and reduce bias—if implemented with care. Use this checklist as your starting point.

1) Disclosures & candidate communication

• Explain where and how automated tools are used in screening
• Provide a contact and process for questions or appeals
• Offer alternatives/accommodations when needed

2) Evaluation design

• Use structured, job-related questions mapped to competencies
• Apply the same rubric across candidates for the same role
• Store rationales with scores for later audit

3) Fairness testing & monitoring

• Run adverse impact analysis by legally protected attributes where lawful
• Monitor pass-through by stage; investigate statistically significant gaps
• Re-test models after material changes or new data

4) Documentation — model & data

• Maintain model cards: purpose, data sources, limitations, updates
• Keep data sheets: collection, retention, access, deletion practices
• Version interview questions, rubrics, and scoring changes

5) Vendor & internal governance

• Ensure vendors provide audit summaries and testing methodology
• Assign internal owners for model updates and acceptance
• Review and sign off on change logs before deployment

6) Jurisdictional readiness

• Prepare AEDT audit support for NYC Local Law 144 if applicable
• Track EU AI Act obligations for “high-risk” employment use-cases
• Align to EEOC guidance emphasizing job-relatedness and consistent use

7) Candidate experience

• 24/7 access; clear instructions; test prompts beforehand
• Keep interviews concise; share next steps and typical timelines
• Provide feedback themes when feasible

The goal

Your aim isn’t to replace human judgment—it’s to scale structured, job-related evaluation while documenting fairness and transparency. That combination earns trust with candidates and regulators alike.