The Adjuster Was an Algorithm: The Policyholder’s Bad-Faith Case Against AI Claim Handling

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The Adjuster Was an Algorithm: The Policyholder’s Bad-Faith Case Against AI Claim Handling

Gallagher & Kennedy shareholder Karin Aldama and Michael S. Levine, a partner at Hunton Andrews Kurth LLP, will present “The Adjuster Was an Algorithm: The Policyholder’s Bad-Faith Case Against AI Claim Handling,” a myLawCLE program examining the legal and litigation issues that arise when insurers use artificial intelligence in the claims-handling process. The live webinar will take place on November 12, 2026, and is eligible for two hours of CLE credit.

The program will explore how traditional insurance bad-faith principles apply when AI systems are used for functions such as automated claim triage, severity scoring, settlement recommendations, fraud detection and claim denials or reductions. Karin and Michael will discuss how attorneys can trace an AI-driven decision from the information provided to the system through its scoring and recommendations to the ultimate claim outcome.

The presenters will also address strategies for obtaining and evaluating the evidence behind algorithmic claim decisions, including risk scores, audit trails, valuation calculations, automated alerts, override logs and model-generated communications. The program will examine discovery considerations involving system architecture, claim-specific AI data, performance validation and corporate knowledge, as well as deposition strategies and potential responses to trade-secret and proprietary-technology objections.

Learn more and and register for this on-demand program at myLawCLE.


about our attorney

Karin Aldama handles complex commercial and business litigation and insurance coverage matters for mid-size to Fortune 500 companies in hospitality, finance, utilities, aerospace, and semiconductors. She helps corporate clients and governmental entities find insurance programs that meet their needs and obtain the coverage to which their policies entitle them.

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