Short answer You can use AI to automate and scale early-stage interview screening — resume parsing, skills/knowledge tests, structured chat or video pre-screens, and candidate ranking — but you must design, validate, monitor, and document the system so it’s job‑relevant, human‑overseen, and legally compliant. Below is a practical, step‑by‑step playbook with what to do, what to watch out for, and where to get more guidance.

Step‑by‑step playbook (practical)

1. Start with the job, not the tool

• Define the role’s critical competencies and measurable success criteria (tasks, skills, credentials, KPIs).
• Map each screening step to one or more job‑relevant signals (e.g., coding test → programming skill; work sample → job performance).
  Why: AI must evaluate job‑related skills to justify decisions and reduce disparate impact. (eeoc.gov)

2. Choose the right use cases for AI Common, lower‑risk AI screening uses:

• Resume parsing + keyword/skills matching.
• Automated pre‑screen questionnaires (structured situational/behavioral questions).
• Job‑relevant tests: coding (CodeSignal, HackerRank style), language/writing, role simulations, work samples.
• Asynchronous text-based interviews (structured questions and NLP scoring). Avoid unvalidated “personality from video” models unless you can justify them. Keep humans in the loop for high‑stakes decisions. (shrm.org, dol.gov)

3. Vendor selection or build decisions

• If you buy, require vendors to share validation/audit reports, fairness testing methods, data provenance, and remediation steps. Don’t outsource legal responsibility — you (the employer) remain accountable. (mcguirewoods.com, eeoc.gov)
• If you build, document training data, features, performance, and testing. Use standard software engineering and MLOps practices.

4. Validate and test for bias before deployment

• Run adverse‑impact tests (e.g., four‑fifths rule and statistical tests) across protected classes; test with representative candidate pools. (eeoc.gov)
• Validate predictive validity: does the AI score predict job performance or a validated proxy? If not, don’t use it to reject candidates.
• Conduct a risk assessment (NIST’s AI Risk Management Framework approach — create a “hiring profile” to identify risks and controls). (jdsupra.com)

5. Operational controls and human oversight

• Human‑in‑the‑loop: require a human reviewer before adverse actions (reject/hire) and for edge cases. (dol.gov)
• Monitoring & drift detection: measure model performance and fairness metrics on an ongoing basis (monthly/quarterly depending on volume).
• Escalation & remediation: define steps when disparate impact or performance degradation appears.

6. Candidate transparency, accommodation & privacy

• Disclose when AI is used and what it does in plain language. Provide contact for questions. (eeoc.gov)
• Provide reasonable accommodations (e.g., alternative assessments) and a way to request them.
• Follow data‑privacy and retention rules; store candidate data securely and only as long as needed.

7. Metrics to track (examples)

• Efficiency: time‑to‑screen, time‑to‑hire, cost‑per‑hire.
• Quality: pass rate → interview → hire conversion, new‑hire performance/retention.
• Fairness: adverse‑impact ratios by demographic group, false positive/negative rates by group.
• Candidate experience: completion rate, drop‑off, NPS.

8. Governance, documentation & legal

• Keep documentation: selection rationale, validation reports, vendor contracts, monitoring logs, accommodation procedures. This helps respond to audits or claims. (mcguirewoods.com, dol.gov)
• Train recruiters and hiring managers on how to interpret and override AI recommendations.

Practical screening workflow (example)

1. Job posted with clear competencies.
2. ATS screens resumes using parsed skills + required criteria.
3. Qualified candidates receive a short automated pre‑screen (structured questions; timed work sample).
4. Scores from tests and pre‑screen go to an initial human recruiter review (human checks AI flags).
5. Top candidates get structured human interviews (same questions for all, same scoring rubric).
6. Final decisions made by panels using documented competency ratings.

Simple rubric example for a pre‑screen answer

• 0 = No relevant response / misses competency
• 1 = Partial evidence (some relevant examples)
• 2 = Clear, job‑relevant example or correct solution Require multiple raters or calibration for human scoring to reduce bias.

What to avoid

• Don’t use AI that infers protected characteristics (race, religion, disability) or relies on proxies that correlate with them without mitigation. (eeoc.gov)
• Don’t use opaque video‑analysis tools (facial expressions, micro‑expressions) as sole decision-makers — these have regulatory and fairness risks.
• Don’t deploy without validation and a plan to monitor and remediate.

Authoritative guidance & resources (key sources)

• EEOC — Artificial Intelligence and Algorithmic Fairness Initiative and technical assistance on adverse impact in selection procedures. (eeoc.gov)
• U.S. Department of Labor / OFCCP — promising practices for contractors using AI, including monitoring, accommodations, and governance. (dol.gov)
• NIST AI Risk Management Framework — a risk‑based approach and playbook for implementing trustworthy AI. (jdsupra.com)
• SHRM — best practices on structured interviewing and how AI can support bias reduction. (shrm.org)
• Legal/industry articles on vendor due diligence and employer liability. (mcguirewoods.com)

Quick checklist to get started (first 30–60 days)

• Document the role and competencies.
• Decide where AI will help (resume screen, test, pre‑screen chat).
• Pilot with a small candidate pool and run fairness/validity tests.
• Design human‑review rules and candidate disclosure language.
• Create monitoring dashboard (accuracy, adverse impact, candidate experience).
• Review vendor contracts for audit access and indemnities.

If you want, I can:

• Draft a simple pre‑screen questionnaire and scoring rubric for a particular role (tell me the role).
• Sketch a monitoring dashboard (metrics and frequency).
• Provide sample disclosure wording for job postings and candidate emails.

(References used above: EEOC guidance on AI & selection procedures; EEOC technical assistance on adverse impact; DOL/OFCCP promising practices for federal contractors; NIST AI RMF resources; SHRM structured interviewing guidance; legal commentaries about vendor due diligence and liability.) (eeoc.gov, dol.gov, jdsupra.com, shrm.org, mcguirewoods.com)