Use Case
AI Candidate Scoring & Ranking Audit
Ensure your AI candidate scoring system ranks candidates fairly across all demographic groups.
Start Your Free AuditThe Challenges
Organizations face significant risks when using AI tools for this purpose without proper bias auditing.
AI scoring models can produce systematically different score distributions across groups
Ranking algorithms may amplify small scoring biases into large selection disparities
Score calibration may differ across demographic subgroups
Intersectional bias in scoring often goes undetected by single-axis analysis
Lack of transparency in how AI models generate candidate scores
The OnHirely Solution
OnHirely performs score distribution analysis using the Kolmogorov-Smirnov test to detect whether your AI tool produces significantly different score distributions across demographic groups. We analyze scoring at every stage of your pipeline to identify where bias enters.
Key Benefits
Detect systematic scoring differences across demographic groups
Identify the pipeline stage where bias is introduced
Ensure score distributions are equitable across protected categories
Meet EU AI Act requirements for algorithmic transparency
Build confidence in the fairness of your scoring methodology
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Read moreReady to Audit Your AI Hiring Tools?
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