Explainable AI for Fairness Audits

Audit credit scoring, loan approval, and insurance underwriting models for disparate impact. We provide SHAP-based explanations to identify discriminatory features and deliver remediation plans that satisfy regulators like the CFPB and OCC.

Key Outcome: Actionable fairness reports for regulatory submission and risk mitigation.

Ensure diagnostic and treatment recommendation models do not perpetuate historical care disparities. Our counterfactual analysis uncovers bias in patient risk stratification, enabling equitable care pathways and supporting compliance with anti-discrimination laws.

Key Outcome: Bias-free clinical decision support tools that uphold ethical care standards.

Scrutinize resume screening, promotion, and compensation algorithms for unintended bias. We deploy LIME and fairness metrics to audit models against EEOC guidelines, providing clear documentation to defend against disparate impact claims.

Key Outcome: Legally defensible AI hiring systems that promote diversity and inclusion.

Analyze pricing, claims, and fraud detection models for fairness across protected classes. We identify proxies for sensitive attributes and provide technical guidance to ensure models are actuarially sound yet non-discriminatory, aligning with state-level regulations.

Key Outcome: Fair pricing models that mitigate regulatory risk and protect brand reputation.

Audit content moderation, ad targeting, and recommendation engines for algorithmic bias. Our fairness audits help prevent the amplification of harmful stereotypes, reduce brand liability, and support the development of more equitable digital ecosystems.

Key Outcome: Responsible AI systems that foster user trust and mitigate reputational damage.