Michigan DIFS outlines expectations for responsible AI use
Regulator highlights potential risks that may come with its opportunities
The Michigan Department of Insurance and Financial Services (DIFS) has issued a bulletin outlining its expectations for insurance carriers regarding the development, acquisition, and use of artificial intelligence (AI) systems.
The bulletin also clarifies the types of information the department may request when investigating or examining a carrier’s use of AI, according to DIFS Director Anita Fox.
In an AM Best report, Fox emphasized the importance of compliance, stating that while AI is transforming various industries, including insurance, all decisions made by insurers using these systems must adhere to applicable federal and state laws and regulations.
This guidance from Michigan follows similar measures recently issued by New York, which focused on preventing unintended discrimination in the use of AI by insurers.
While AI has driven product innovation and streamlined many insurance processes, DIFS highlighted potential risks associated with AI, including inaccuracies, discrimination, and challenges in explaining internal processes.
To mitigate these risks, the regulator advised that carriers establish AI governance frameworks emphasizing transparency, fairness, and accountability while protecting trade secrets and proprietary information.
The bulletin recommends that carriers document AI system validation, auditing, and testing procedures, including evaluations of the reliability of AI outputs that could impact decision-making.
Additionally, when using third-party AI systems and data, carriers are advised to perform due diligence on potential partners and their data, models, and systems, with documentation of these processes being essential for regulatory investigations.
DIFS noted that while the bulletin provides guidance on AI use and the investigative process, it is not exhaustive. Carriers are allowed to present alternative methods to demonstrate compliance, and the bulletin is not intended to prescribe or endorse any specific practice or system.
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