A tutoring company's recruitment software automatically rejected female applicants over 55 and male applicants over 60.
In August 2023, the US Equal Employment Opportunity Commission settled the first AI-hiring discrimination case under federal anti-discrimination law. iTutorGroup paid US$365,000 after its automated recruitment software was found to have rejected more than 200 qualified applicants on the basis of age alone — women aged 55+ and men aged 60+. The settlement established that deploying a third-party hiring AI does not insulate an employer from liability for discriminatory outcomes.
What caused this
iTutor Group used an AI-powered resume-screening tool that automatically rejected female applicants aged 55+ and male applicants aged 60+. The US EEOC found the tool encoded age discrimination directly into its scoring rules, violating the ADEA. The company settled for USD 365,000.
- Hard-coded age cut-offs treated as a "business rule" rather than a protected-attribute decision.
- No bias testing across protected classes before deployment.
- No human review of automatic rejections.
- No record-keeping that would let an applicant or regulator audit why they were screened out.
How this could have been avoided
Hiring is a high-risk use case under the EU AI Act and a litigation hotspot under US employment law. Govern it accordingly.
- Protected-attribute review — no model feature, rule or proxy may use age, gender, race, disability or related variables without documented legal justification.
- Pre-deployment bias audit (NYC LL 144, EU AI Act Art. 10 data governance, ISO/IEC 42001 Annex A.6.2.4) with public summary.
- Human-in-the-loop on all adverse decisions; candidates can request human review.
- Logging and explainability — store inputs, scores and reasons for every decision for the statutory retention period.
- Vendor due diligence — contractual right to audit the model and training data of any HR-tech supplier.
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