Enterprise AI Hiring Governance

Independent Algorithmic Bias Auditing for Enterprise Recruiting

Third-party AI screening tools can quietly filter candidates based on age, race, and disability without ever asking for protected attributes. Fraoula provides independent, continuous mathematical verification across Workday, Greenhouse, and custom ML talent pipelines.

Interactive Four-Fifths (80%) Rule Compliance Calculator

Evaluate adverse impact under Title VII, ADEA (Age 40+), and EEOC Uniform Guidelines in real time.

Client-Side Simulation
▶ Benchmark Group (e.g. Under 40)
7000
1260
Selection Rate: 18.00%
▶ Protected Cohort (e.g. Age 40+, Minority)
3000
330
Selection Rate: 11.00%
Disparate Impact Ratio (DIR)
0.611
Violates 4/5ths Rule (<0.80)

Protected group is selected at only 61.1% the rate of the benchmark group. Under EEOC guidelines, this establishes prima facie adverse impact liability.

Statistical Significance: Z = -9.28 (p < 0.0001)
Technical Architecture

The 4 Pillars of Fraoula Algorithmic Recruiting Governance

Enterprise AI recruiting compliance requires more than annual vendor whitepapers. Fraoula Data Auditor evaluates models as empirical black boxes through continuous statistical hypothesis testing.

PILLAR 01

Continuous Adverse Impact & 4/5ths Rule Telemetry

Real-time calculation of the Disparate Impact Ratio across age (ADEA 40+), gender, race, and disability. Automated circuit breakers halt automated screening on specific roles if the rolling 30-day DIR falls below safe thresholds (0.85).

PILLAR 02

Latent Proxy Feature Attribution (SHAP Decomposition)

Machine learning models reconstruct protected attributes through high-dimensional correlates. Fraoula uses game-theoretic SHAP values to identify when features like college graduation year, vintage technology mentions (e.g., COBOL, VB6), or career gaps drive negative scoring weights.

PILLAR 03

Counterfactual Resume Permutation Auditing

Fraoula programmatically generates synthetic resume pairs where skills, qualifications, and domain experience remain identical while latent proxy tokens (graduation year, high school date, career gap placement) are systematically varied to measure algorithmic score variance.

PILLAR 04

Rejection Velocity & Off-Hours Anomaly Throttling

Automated algorithms often reject candidates within minutes in the middle of the night (e.g., 3:15 AM). Fraoula tracks rejection velocity and enforces a human-in-the-loop review buffer, ensuring no candidate is discarded by unmonitored batch inferences without human oversight.

Legal & Regulatory Alignment

Full Regulatory Coverage Across Global AI Hiring Mandates

You cannot outsource Title VII or ADEA liability to software vendors. Fraoula provides the verifiable mathematical paper trail demanded by federal enforcement agencies.

Regulatory Framework Governing Body Compliance Mandate Fraoula Automated Audit Module
Title VII (Civil Rights Act of 1964) EEOC Zero unlawful disparate impact on race, sex, national origin Automated UGESP 4/5ths Rule continuous monitoring
ADEA (Age Discrimination in Employment Act) EEOC / Federal Courts Prohibits discrimination against applicants 40 and older Graduation year proxy redaction & counterfactual age audit
ADA (Americans with Disabilities Act) EEOC / DOJ Prohibits pre-employment screening that filters out disabilities Audit of game-based cognitive tests & neuroticism proxies
NYC Local Law 144 (AEDT) NYC DCWP Mandatory annual independent bias audit published publicly Certified independent AEDT audit report generation
EU AI Act (Annex III) European Commission Recruitment AI classified as High-Risk (Art. 10 data governance) Article 10 bias testing & technical documentation dossier

Is Your AI Screening System Legally Defensible?

Do not wait for an EEOC subpoena or federal class action to discover proxy bias in your third-party hiring tools. Protect your enterprise with Fraoula Data Auditor.

Request Enterprise AI Audit → Read The Workday Deep Dive