Here’s a practical, compliance‑aware playbook for using AI for voice interview screening — from goals and architecture to legal/ethical safeguards, technical choices, evaluation, and deployment.

Summary (one line)

• Use AI to transcribe and summarize/triage candidate audio, not to make sole hiring decisions; build a human‑in‑the‑loop screening pipeline, validate for fairness, and document consent/records.

1. Define scope and goals

• Decide exactly what AI will do (examples: transcribe answers, extract skills/keywords, score answer completeness vs. a rubric, flag follow‑ups for recruiters).
• Avoid using AI to infer protected characteristics (race, ethnicity, gender identity, disability) or to make final pass/fail hiring decisions without human oversight and validation. (Regulators treat automated selection tools under anti‑discrimination laws.) (eeoc.gov)

2. Candidate experience, consent, and accommodation

• Tell candidates you’ll use automated transcription/analysis and what you will measure. Offer an alternative (text interview or live human interview) and a clear accommodation process (ADA). (eeoc.gov)
• Get explicit consent to record and analyze audio; record the consent and keep retention periods minimal.

3. Suggested system architecture (pipeline)

• Capture audio (good mic, noise suppression).
• Speech-to-text (ASR) with confidence scores and word timestamps. (Store raw audio and transcripts securely.) (github.com)
• Text processing: apply a structured scoring rubric (keywords, STAR elements, completeness, role‑specific answers) using NLP models.
• Optional: limited paralinguistic signals (speech rate, long pauses) only if rigorously validated for the role and accommodations; avoid emotion or personality inference unless independently validated and legally safe. (These features are controversial and often biased.) (shrm.org)
• Human review: recruiters review AI summaries and any borderline/flagged cases before advancing or rejecting. Log all decisions and model outputs.

4. Choosing ASR and tools (practical notes)

• Options: open models (OpenAI Whisper), cloud services (Google Cloud Speech‑to‑Text, Amazon Transcribe) — compare accuracy on your audio, cost, latency, languages, speaker‑diarization, timestamps, and data‑use policies. Always test with representative accents and audio quality. (github.com)
• Important: off‑the‑shelf models can hallucinate or transcribe incorrectly — test for error modes and plan human QA for critical text. (apnews.com)

5. Data, fairness, and legal compliance

• Legal context: U.S. employment laws (Title VII, ADA) apply to automated selection tools — employers must evaluate adverse impact and provide accommodations. Vendors and customers can be liable. Follow EEOC/DOJ guidance on using software for selection. (eeoc.gov)
• Audit for bias: measure performance across demographics (selection rate, false negative/positive rates, transcription Word Error Rate by accent/language, model confidence). Use the four‑fifths rule and other statistical tests as starting points — but follow EEOC/NIST guidance for detailed analyses. (nist.gov)
• Avoid inferring protected attributes for decisioning. If you must evaluate fairness then use self‑reported demographic data for auditing (with consent) or an independent auditor; document methods and limitations.

6. Validation and metrics to track

• Transcription: Word Error Rate (WER) overall and by subgroups (accent, non‑native speakers, audio quality).
• NLP scoring: inter‑rater reliability between AI score and human score (Cohen’s kappa), precision/recall for skill detection.
• Downstream hiring metrics: predictive validity (are screened‑in candidates more likely to succeed in role?), adverse impact ratio, selection rates, appeal/complaint rates.
• Operational: latency, throughput, cost per interview, % needing human review.

7. Human‑in‑the‑loop and governance

• Use AI for triage/summarization, not final hiring decisions. Maintain human reviewers for all rejections or for any automated negative signals.
• Create governance: owner, documented model versions, datasets used, validation reports, change control, and periodic re‑audit schedule (quarterly or after major model changes). Follow NIST AI RMF practices for governance and risk management. (nist.gov)

8. Privacy, security, and vendor contracts

• Encrypt audio in transit and at rest, minimize retention, and limit access. Specify data deletion/portability in candidate notices and vendor contracts.
• Contractually require vendors to: provide model transparency, share validation/bias testing, commit not to sell candidate data, and support audits.

9. Practical implementation checklist (quick)

• Build a structured interview rubric for each role.
• Pilot on a labeled dataset (past interview audio with hiring outcomes) and measure predictive validity.
• Compare multiple ASR providers on your audio and choose best fit. (github.com)
• Run bias audits (transcript errors by accent; score differences by subgroup).
• Add candidate consent & alternative path.
• Deploy with human review and audit logging; re‑evaluate regularly.

10. Pitfalls to avoid

• Don’t rely on emotion/face/voice inference for hiring decisions — scientific validity is weak and regulatory risk is high. (shrm.org)
• Don’t claim the AI “replaces human judgment” — FTC enforcement targets deceptive claims about AI capabilities. Document evidence for any efficacy claims. (ftc.gov)
• Don’t skip subgroup testing — ASR and NLP performance commonly vary by accent/language and may