AI screening is rejecting applicants within hours. HR can fix this.
Job seekers are getting knocked back within minutes of hitting "apply." One recent example: Victoria-based HR professional Leighan Morrell said she was rejected two hours after applying and suspected an automated screen.
Speed is useful. Blind speed is risky. Over-relying on scoring algorithms and keyword filters can bake bias into your funnel, damage your brand, and quietly violate fair hiring laws.
What's going wrong
- Keyword-heavy filters that miss transferable skills and non-linear careers.
- Unvalidated scores treated as truth, without checking job relevance.
- Hidden proxies for protected attributes (school names, postcodes, career gaps).
- Instant auto-rejections that offer no human review or appeal path.
- Vendors that can't show bias testing, data sources, or error rates.
A responsible AI hiring playbook
- Define job evidence first. Lock criteria to the work: outcomes, must-have skills, and acceptable evidence (work samples, assessments, structured interviews). No vibes.
- Validate before scale. Run a local validation study. Check correlation with performance and apply the 80% (four-fifths) rule for adverse impact across key groups.
- Keep a human in the loop. Any "reject" within 24 hours gets a manual review. Require a second look for candidates within 10% of your cutoff score.
- Disclose automation. Tell candidates when AI is used and how they can request a human review. Offer a simple appeal path.
- Cut obvious proxies. Strip features like name, age signals, addresses, graduation year, or school prestige unless you can justify job relevance.
- Vendor diligence. Ask for model cards, bias tests, local pilot results, data sources, privacy posture, and retention periods. Secure audit rights and raw logs.
- Monitor monthly. Track pass rates by stage and demographic, false negatives, and quality of hire. Watch drift after every model or JD change.
- Throttle automation. Use AI to triage, not to gatekeep. Let it prioritize the review queue, not decide final outcomes.
- Offer a human fallback. Provide a structured phone screen or work sample option for flagged candidates.
- Document everything. Criteria, approvals, fairness checks, and outcomes. If you can't show your work, you don't have a defense.
Practical policies you can ship this week
Automated Screening Policy (one-pager)
- Purpose: Speed up review while protecting fairness and legal compliance.
- Scope: Roles using automated scoring or filters.
- Controls: Human review for fast rejections and near-threshold scores.
- Data: No proxies; retention aligned with privacy rules; vendor logs required.
- Fairness: Monthly adverse impact checks; quarterly validation refresh.
- Candidate Rights: Disclosure, appeal path, and contact email.
Rejection email (short and clear)
"Thanks for applying to [Company]. We used both human review and automated tools to assess fit for [Role]. Based on the criteria, we're not moving forward now. If you'd like a human review of this decision, reply to this email within 7 days."
Bias audit checklist
- Job-related criteria are defined and approved.
- Model/filters tested on local data; performance vs. job outcomes documented.
- Adverse impact ratio calculated for each stage and group.
- Rejected features: proxies and sensitive attributes confirmed removed.
- Candidate disclosure reviewed by legal and privacy.
- Monitoring dashboard and escalation playbook in place.
Legal and ethics quick hits
- EEOC guidance on AI and selection procedures (adverse impact and Title VII): Read the advisory
- Australian privacy guidance on AI and automated decisions: AI and privacy
Team capability
Your recruiters and hiring managers need working knowledge of AI basics, bias testing, and structured selection. Upskill them so the tech serves your process, not the other way around.
Bottom line
Speed without judgment is a liability. Use AI to surface the right signals, then make fair, documented, human decisions. Candidates will feel the difference - and so will your compliance reports.
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