Third-Party AI Vendor Bias Assessment

Receive a detailed statistical report on potential disparate impact across protected attributes (race, gender, age), providing a defensible, data-driven basis for vendor negotiations and compliance reporting under frameworks like the EU AI Act.

Transform vendor selection from a feature-checklist exercise into a rigorous technical evaluation. We provide objective evidence to support or challenge vendor claims, protecting your organization from costly integration failures and reputational damage.

Leverage our audit findings to establish concrete fairness performance benchmarks and Service Level Agreements (SLAs) in vendor contracts, creating enforceable accountability for bias mitigation over the system's lifecycle.

Generate the necessary technical documentation and audit trails to demonstrate proactive governance to regulators (e.g., for EU AI Act high-risk systems) and internal stakeholders, reducing legal exposure and streamlining compliance reviews.

Go beyond identifying problems. We deliver a prioritized, technical action plan for the vendor—or for your integration team—detailing steps for model retraining, data curation, or architectural changes to achieve fairness targets. Learn more about our approach to Fairness-Aware Model Training.

Proactively prevent public relations crises and loss of customer trust caused by a biased vendor AI system operating under your brand. An independent audit is a critical component of responsible AI stewardship. For comprehensive governance, explore our Enterprise AI Governance and Compliance Frameworks.

Audit credit scoring, loan approval, and insurance underwriting algorithms for disparate impact against protected classes. We assess compliance with the Equal Credit Opportunity Act (ECOA) and Fair Lending laws.

Key Deliverables: Disparate impact ratio analysis, counterfactual fairness testing, and risk-weighted bias scorecards for vendor selection.

Evaluate resume screening, video interview analysis, and promotion recommendation systems for demographic parity and adverse impact. We ensure alignment with EEOC guidelines and OFCCP regulations.

Key Deliverables: Four-fifths rule analysis, subgroup performance parity reports, and recommendations for fairness-aware retraining or vendor replacement.

Assess diagnostic AI, patient risk stratification, and treatment recommendation tools for racial, gender, and socioeconomic bias that could lead to inequitable care outcomes and violate anti-discrimination provisions.

Key Deliverables: Clinical outcome disparity mapping, calibration fairness across groups, and integration checks for HIPAA-compliant audit trails.

Scrutinize recidivism prediction, facial recognition, and forensic analysis tools for accuracy disparities across demographics. We provide technical validation against the NIST FRVT findings and emerging state AI regulations.

Key Deliverables: False positive/negative rate analysis by subgroup, model card documentation, and compliance gap analysis for predictive policing tools.

Analyze algorithmic ad delivery, customer segmentation, and dynamic pricing engines for unintended discrimination in housing, employment, or credit opportunities, addressing risks under the Civil Rights Act.

Key Deliverables: Delivery bias analysis across platforms, lookalike audience fairness scoring, and technical remediation plans for vendor SDKs.

Provide third-party validation for AI systems procured by federal, state, and local agencies, ensuring they meet mandates for algorithmic fairness, transparency, and accountability before public deployment.

Key Deliverables: Independent verification against the NIST AI RMF, EU AI Act high-risk classification checks, and procurement-ready fairness assessment reports.