Yes, automated hiring software can reject qualified applicants. A 2022 U.S. Equal Employment Opportunity Commission (EEOC) lawsuit alleged that iTutorGroup’s application software automatically rejected more than 200 qualified U.S. applicants because of age. The case shows a documented failure of automated screening—not proof that every hiring product is biased or that any particular system has a known error rate.
What the iTutorGroup case actually showed
In a May 5, 2022 lawsuit announcement, the EEOC said iTutorGroup had programmed its tutor-application software in 2020 to reject female applicants aged 55 or older and male applicants aged 60 or older. The agency said more than 200 qualified applicants in the United States were rejected because of age.
The mechanism described by the EEOC was a programmed age cutoff. The public allegation does not establish that the system was generative AI or a sophisticated machine-learning model. It is more precise to describe it as automated application software used in hiring.
On September 11, 2023, the EEOC announced that iTutorGroup would pay $365,000 and provide other relief to settle the suit. The settlement announcement described monitoring and injunctive measures.
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| Fact | What the EEOC reported |
|---|---|
| Screening rule | Female applicants aged 55 or older and male applicants aged 60 or older were allegedly rejected automatically. |
| Applicants affected | More than 200 qualified applicants in the United States, according to the EEOC’s 2022 lawsuit announcement. |
| Settlement | $365,000, announced by the EEOC on September 11, 2023, plus other relief. |
| Technology description | Application software programmed to apply an age-based rule; the EEOC release did not identify it as generative AI. |
Why an expert says qualification can be missed
Futurism’s February 17, 2024 report quoted Hilke Schellmann, an NYU journalism professor and author, speaking to the BBC: “We haven’t seen a whole lot of evidence that there’s no bias here… or that the tool picks out the most qualified candidates.” That is an assessment about the state of evidence, not a finding that every vendor’s system discriminates.
Hiring technology covers different functions that should not be treated as one product category:
- Screening: applies rules or minimum requirements to applications.
- Ranking: prioritizes résumés for recruiter review.
- Assessments: scores tests, work samples or questionnaires.
- Interview analysis: evaluates recorded or live interview responses.
A failure in one function cannot establish the error rate or fairness of another. The available evidence also does not provide a representative, cross-employer estimate of how often qualified candidates are rejected.
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Can employers legally use automated screening?
Employers can use software in selection decisions, but federal employment-discrimination laws still apply. EEOC technical assistance published in May 2023 addresses adverse impact in software, algorithms and artificial intelligence used in selection procedures under Title VII. Employers remain responsible for their practices even when a vendor’s system makes or influences a decision.
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These are U.S. federal examples and guidance. State, local and international rules can add requirements, so an employer should verify the law that applies to its workforce and applicants.
How job seekers can respond to an automated rejection
1. Read the application instructions closely
Look for minimum qualifications, knockout questions, timed tests, résumé-format requirements and notices that an automated assessment will be used. A rejection after a knockout question may be a rule-based screen rather than a holistic review of your résumé.
2. Ask about accommodation when a disability may affect the process
If a disability could affect a test, interview format or other automated step, contact the employer using the instructions in the posting or application portal. Ask whether an accommodation or alternate assessment is available. The sources discussed here do not establish a universal right to a particular human review or guaranteed appeal route, so do not assume one exists.
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Save the job posting, application confirmation, assessment instructions, rejection notice and relevant correspondence. Note dates, the questions asked and any technical problems. These records can help you ask a precise question or seek advice about a possible discrimination issue.
4. Ask a focused question
You can ask whether the application was rejected by an automated rule, whether a recruiter reviewed it, and whom to contact about an accommodation or process concern. An employer may not disclose proprietary scoring details, but a clear question can identify the appropriate contact.
5. Consider whether a pattern exists
One rejection does not prove that software discriminated. A repeated pattern involving a protected characteristic, an explicit cutoff or an admission that an automated rule caused the result may justify obtaining advice from an employment lawyer, worker advocate or the appropriate government agency.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions employers should answer before buying or deploying a tool
EEOC guidance makes adverse-impact assessment a practical governance issue, not merely a vendor-marketing question. Before deployment and during monitoring, an employer should be able to answer:
- What drives the decision? Identify job-related criteria, data fields, tests, weighting and any automatic rejection rules.
- What does the system do? Distinguish automatic rejection from ranking or flagging for human review.
- How is adverse impact assessed? Define the relevant applicant groups, comparison method, review period and escalation process.
- How are accommodations handled? Provide a way for applicants to request an alternate process when a disability affects the tool’s operation.
- Who reviews exceptions? Specify when a trained human examines a result and who can override an erroneous rule.
- What records are retained? Preserve inputs, outputs, rule or model versions, validation results, complaints and decisions so the employer can explain and monitor outcomes.
- What does the contract require? Assign responsibilities for testing, incident reporting, data retention, updates and cooperation with investigations.
What this evidence does—and does not—prove
- It proves that an automated hiring process can encode an unlawful age cutoff and exclude qualified applicants.
- It supports treating employer oversight, testing and recordkeeping as essential.
- It does not establish that all AI hiring tools are biased.
- It does not reveal a universal percentage of qualified candidates rejected by such systems.
- It does not show that every résumé screen, assessment or interview-analysis product uses the same technology.
Futurism also reported a makeup-artist assessment example and repeated a statistic about companies using AI in human-resources work, attributing both to BBC reporting and, for the statistic, an IBM survey. Those details were not independently verified against the underlying primary sources and should not be treated as established prevalence evidence.
Bottom line for your career and finances
An automated rejection can be a screening event rather than a definitive judgment about your qualifications. Keep records, request an accommodation or alternate assessment when relevant, and ask whether a human reviewed the application. For employers, automation does not transfer legal responsibility: the organization must understand, test and monitor the selection process it uses.
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