
Fairness auditing for AI-powered video interviews, facial analysis, and automated candidate assessment.
AI-powered video interviews promise to assess candidates objectively, analyzing everything from facial expressions to word choice. But research consistently shows these systems discriminate against candidates based on race, disability, and cultural background.
Facial recognition technology performs significantly worse on darker skin tones. Expression analysis penalizes neurodivergent candidates. Even background and lighting can affect scores, disadvantaging candidates without professional home office setups.
We test video interview AI to expose these biases and help you build fairer assessment processes.

Common bias patterns we test for in video interview AI systems
AI facial analysis performs worse on darker skin tones and non-Western facial features.
Candidates with visible disabilities, facial differences, or atypical expressions may be unfairly scored.
Candidates without professional lighting or cameras may receive lower scores regardless of competence.
Eye contact, facial expressions, and gestures vary by culture—AI may penalize non-Western norms.
Autistic candidates and others with different communication styles may be systematically filtered out.
Combined scoring of voice and video can compound biases from both modalities.
A comprehensive approach to testing video interview AI for fairness
Identify what visual features the AI actually analyzes and weights.
Test scoring across diverse skin tones, ages, and physical characteristics.
Assess how lighting, backgrounds, and equipment affect scores.
Statistical analysis of score correlations with protected characteristics.
Detailed analysis of scoring patterns across demographic groups.
Understanding of which visual features most influence AI decisions.
Recommendations for fairer video interview processes.
Discover hidden biases in your video assessment process.
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