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🔍 '''Bias audit''' is a systematic evaluation of an [[Definition:Algorithm | algorithm]], [[Definition:Predictive model | predictive model]], or automated decision-making tool used in the [[Definition:Insurance | insurance]] industry to determine whether it produces outcomes that unfairly discriminate against protected groups — such as those defined by race, gender, ethnicity, or other characteristics prohibited under [[Definition:Unfair discrimination | unfair discrimination]] laws. As insurers and [[Definition:Insurtech | insurtech]] companies increasingly rely on [[Definition:Artificial intelligence (AI) | artificial intelligence]] and [[Definition:Machine learning (ML) | machine learning]] for [[Definition:Underwriting | underwriting]], [[Definition:Claims adjudication | claims handling]], [[Definition:Pricing | pricing]], and [[Definition:Fraud detection | fraud detection]], bias audits have emerged as a critical governance practice to ensure these tools comply with regulatory standards and ethical expectations.

⚙️ Conducting a bias audit typically involves testing a model's outputs across demographic segments to identify statistically significant disparities. Auditors — who may be internal [[Definition:Data science | data science]] teams, [[Definition:Compliance | compliance]] officers, or independent third parties — compare approval rates, [[Definition:Premium | premium]] levels, [[Definition:Claim denial | claim denial]] frequencies, or other decision outputs for different groups, then assess whether any observed disparities are actuarially justified or reflect prohibited proxy discrimination. Techniques include [[Definition:Disparate impact analysis | disparate impact analysis]], sensitivity testing of input variables, and examination of training data for historical biases. In jurisdictions like New York, Local Law 144 requires bias audits for automated employment decision tools, and insurance regulators at both state and federal levels are developing analogous expectations for [[Definition:Rating algorithm | rating algorithms]] and [[Definition:Automated underwriting | automated underwriting]] systems.

🛡️ Beyond regulatory compliance, rigorous bias auditing protects carriers from [[Definition:Litigation risk | litigation]], reputational damage, and market access restrictions. A model that inadvertently charges higher [[Definition:Premium | premiums]] to minority communities or disproportionately flags certain groups for [[Definition:Fraud | fraud]] investigation can expose an insurer to enforcement actions by state [[Definition:Department of insurance | departments of insurance]] and federal agencies. The [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] has established a working group on AI and predictive analytics that specifically addresses the need for transparency and fairness testing. For [[Definition:Insurtech | insurtech]] firms whose value propositions rest on algorithmic sophistication, demonstrating that products have passed independent bias audits is increasingly a prerequisite for partnering with established carriers and gaining regulatory approval for new products.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Predictive model]]
* [[Definition:Unfair discrimination]]
* [[Definition:Automated underwriting]]
* [[Definition:National Association of Insurance Commissioners (NAIC)]]
* [[Definition:Algorithmic accountability]]
{{Div col end}}

Definition:Bias audit - Revision history

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