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No—not on the evidence described in the headline. A University of Pennsylvania study reportedly used AI to estimate personality traits from LinkedIn profile photos and compared those estimates with career outcomes. That is an association in a selected group, not proof that a face scan can accurately tell an employer whether a particular applicant should be hired.
What the reported study did—and did not show
Futurism reported on November 9, 2025, that researchers used a system informed by earlier face-based personality research to estimate the Big Five traits—openness, conscientiousness, extraversion, agreeableness and neuroticism—from LinkedIn headshots of 96,000 MBA graduates. The reported analysis then compared the estimates with career outcomes.
Futurism said estimated extraversion had the strongest positive association with compensation, while estimated openness was associated with lower pay. Those are the article’s descriptions of the findings, not independently verified effect estimates. The linked paper was not accessible for review, so its sample construction, model details, validation design, demographic breakdowns and limitations cannot be confirmed here. Read Futurism’s report.
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Even if the reported associations are accurate, they do not establish that facial features cause higher or lower pay. Nor do they show that the model can predict an individual’s job performance, compare applicants fairly, or identify who should be hired. The study, as described, examined estimated traits and career outcomes among MBA graduates; it was not a prospective hiring test across occupations.
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Can AI tell someone’s personality from a face?
The report describes a model estimating personality traits from photographs, but a model output is not the same as a reliable measurement of a person’s character. A correlation between a model’s estimate and an outcome does not prove the estimate is accurate for each person, or that it adds useful information beyond job-related qualifications and experience.
The distinction matters in hiring: a result that tracks a group-level pattern may still be too uncertain, biased or irrelevant to support an individual decision. The available account does not establish the system’s accuracy or performance across demographic groups, and it does not show that the estimated traits are necessary for any particular job.
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Do hiring tools analyze faces, voices or expressions?
Some employment technologies may analyze video, facial movement, voice or speech. Their use and capabilities vary, so a report about one research system should not be taken to mean that every employer scans applicants’ faces or that a given interview platform makes hiring decisions this way.
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The UK government’s Responsible AI in Recruitment guidance warns that face, voice and speech analysis can raise disparate-accuracy and accessibility concerns. It also notes little scientific consensus on inferring emotion from facial expression. An apparent expression or vocal pattern should not be treated as a dependable reading of a candidate’s feelings or suitability without strong evidence for the specific use.
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Why hiring decisions need stronger evidence
The Society for Industrial and Organizational Psychology (SIOP) says AI-based employment assessments should meet core standards for job-related assessment: they should accurately predict relevant outcomes, produce consistent scores that reflect job-related characteristics, be fair, and have documentation sufficient for verification and audit. Its recommendations for AI-based assessments offer a practical way to scrutinize vendor claims.
A general fairness review is not enough by itself. An employer also needs evidence that the assessment measures something relevant to the actual role and predicts an appropriate outcome in that employer’s context. A tool that performed acceptably in a supplier’s data may behave differently with a different applicant pool, job or deployment process.
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Disability access and discrimination risks
In the United States, the Department of Justice says the Americans with Disabilities Act applies to all parts of employment, including selection and testing. Its guidance warns that facial and voice analysis could screen out qualified applicants with autism or speech impairments. Employers remain responsible for disability discrimination even when they use a third-party product.
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Rules also differ by jurisdiction and use. The EU AI Act’s consolidated text lists certain workplace emotion-inference uses among prohibited practices, with an exception for medical or safety reasons. That provision should not be read as resolving the legal status of every facial-analysis system or hiring assessment. Consult the applicable law and its implementation details for a specific deployment: Regulation (EU) 2024/1689, consolidated text.
How employers should evaluate an AI hiring tool
Before relying on a system that analyzes a face, voice or other video signal, employers should ask for evidence about the exact job and use—not just a general claim that the product is “AI-powered” or unbiased.
- Job relevance: What job-related characteristic or outcome is the tool meant to assess, and why is that relevant to this role?
- Validation: Does evidence show that the score predicts the relevant outcome for this job and applicant population? How was that tested?
- Consistency and fairness: Are scores reliable, and have differences in accuracy or impact across groups been assessed?
- Accessibility: Could the method disadvantage applicants because of disability, speech, appearance or another factor unrelated to the work? What accommodations are available?
- Monitoring: Is the system checked regularly for bias or degraded performance in the employer’s own setting, including during a pilot?
- Documentation and accountability: Can the employer verify how the system was developed, scored and interpreted, and who is responsible for the decision?
The UK guidance advises organisations to assess bias, monitor for performance degradation, and consider impact and data-protection assessments as appropriate. It also cautions that supplier data may not represent an employer’s context and that issues can emerge in a pilot. These checks complement, rather than replace, evidence that a score is valid for the job.
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The headline refers to research reported as linking machine-estimated traits from LinkedIn photos with career outcomes. It does not establish that a company can reliably decide whether an individual should be hired by scanning their face. If an employer uses an AI assessment, applicants can ask what it evaluates, how the result is used, and how to request an accommodation if the format creates a disability-related barrier. The answer may depend on the employer, the tool and local law.
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