Detect & Eliminate AI Hiring Bias
Our advanced bias detection engine analyzes your AI hiring tools for discrimination across race, gender, age, disability, and intersectional categories. Ensure fairness and compliance automatically.
Bias Detected
Your AI tool shows adverse impact against female candidates
Below 0.80 threshold - indicates adverse impact
Recommended Actions:
- • Review training data for gender balance
- • Remove proxy variables (e.g., employment gaps)
- • Implement fairness constraints
AI Bias is a Critical Compliance Risk
Undetected bias in AI hiring tools leads to discrimination lawsuits, regulatory penalties, and reputational damage
of AI hiring tools show measurable bias
average discrimination lawsuit settlement
of companies don't test for AI bias
LL144 requires annual bias audits
We Test for All Types of Bias
Our bias detector analyzes your AI tools across multiple protected characteristics
Gender Bias
Discrimination based on sex, gender identity, or pregnancy status
Racial Bias
Disparate impact across racial and ethnic groups
Age Discrimination
Bias against older or younger candidates
Disability Bias
Discrimination against candidates with disabilities
Socioeconomic Bias
Bias based on education, zip code, or background
Intersectional Bias
Compounded discrimination across multiple categories
Advanced Bias Detection Methodology
We use industry-standard fairness metrics and statistical analysis
Adverse Impact Analysis
Calculate selection rates and impact ratios using the four-fifths rule (EEOC standard).
Formula:
Impact Ratio = (Protected Group Rate) / (Highest Group Rate)
Pass: ≥ 0.80
Disparate Impact Testing
Statistical significance testing to identify patterns of discrimination.
Methods:
- • Chi-square tests
- • Fisher's exact test
- • Z-score analysis
Intersectional Analysis
Test for compounded bias across multiple protected characteristics.
Examples:
- • Black women vs White men
- • Asian men vs White women
- • Older Hispanic candidates
Comprehensive Bias Audit Features
What We Analyze
Selection Rates
Percentage of candidates selected from each demographic group
Impact Ratios
Comparison of selection rates between protected and reference groups
Scoring Distributions
How AI scores are distributed across demographic groups
Feature Importance
Which features contribute most to biased outcomes
What You Get
Detailed Bias Report
Comprehensive analysis with statistical significance
Compliance Documentation
Ready for NYC LL144, EEOC, and EU AI Act requirements
Mitigation Recommendations
Specific steps to reduce or eliminate detected bias
Continuous Monitoring
Track bias metrics over time and get alerts