Can insurance AI models be audited for bias

Yes, Through Established and Emerging Audit Techniques

Insurance AI models genuinely can be audited for bias, using techniques that analyze model outcomes across different demographic groups and examine specific model inputs for potential discriminatory effects — and a growing number of states now require or encourage this kind of auditing as part of formal insurance regulatory oversight.

Analyzing Outcomes Across Demographic Groups

A core auditing technique involves systematically comparing a model’s actual outcomes — premium levels, approval rates, or other relevant measures — across different demographic groups to check for statistically significant disparities that might indicate the model is producing meaningfully different results for similarly situated individuals based on protected characteristics.

Examining Specific Inputs for Proxy Discrimination

Beyond outcome analysis alone, more thorough audits also examine specific model inputs for potential proxy discrimination effects, checking whether a seemingly neutral factor used in the model closely correlates with a protected characteristic in a way that could produce discriminatory effects even without directly using that protected characteristic as an explicit input.

Why a Growing Number of States Now Require or Encourage This Auditing

Reflecting growing regulatory recognition of documented bias risks in AI-based insurance models, a growing number of states have introduced requirements or strong encouragement for insurers to conduct this kind of bias auditing as part of their regulatory rate filing and review process, adding a more formalized, systematic check beyond simply relying on an insurer’s own internal assurances of fairness.

Why Auditing More Complex Models Presents Genuine Technical Challenges

Auditing more sophisticated AI models can be technically more challenging than auditing simpler traditional actuarial models, since these more complex models may incorporate many combined data factors with non-obvious interactions between them, making it harder to fully trace exactly how the model reaches a specific outcome or to comprehensively identify every potential, subtle source of bias.

Why Both Internal and Independent Third-Party Audits Play a Role

Bias audits can be conducted internally by an insurer’s own data science and compliance teams, or by independent third-party auditors, with the latter generally providing a more objective, externally verifiable assessment, particularly valuable in response to specific regulatory requirements or for insurers seeking to demonstrate genuine, credible commitment to fairness beyond internal self-assessment alone.

Bottom Line

Insurance AI models can genuinely be audited for bias, using techniques that analyze outcomes across demographic groups and examine specific inputs for proxy discrimination effects, with a growing number of states now requiring or encouraging this kind of auditing — though auditing more sophisticated, complex AI models presents genuine technical challenges compared to auditing simpler traditional actuarial models.

Go deeper

• How Do Companies Test AI Models for Bias Before Release?
• Can AI Credit Scoring Be Biased Against Certain Groups?
• Can AI Bias Be Completely Eliminated?