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Insurers Must Ensure They Develop And Use AI Ethically: WTW’s Chapman

Insurers Must Ensure They Develop And Use AI Ethically: WTW’s Chapman

Insurers Must Ensure They Develop And Use AI Ethically: WTW’s Chapman

Insurers Must Ensure They Develop And Use AI Ethically: WTW’s Chapman

In the insurance and reinsurance industries, artificial intelligence (AI) and machine learning are hot issues. More businesses are utilizing the technology to manage manual, low-complexity operations and significantly boost operational efficiency.

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The technology also makes it possible to predict losses and customer behavior with greater accuracy; some insurers claim that this gives them more opportunities to change customer behavior and even stop claims from occurring.

Despite everything, there are risks associated with this technology, which Neil Chapman, Senior Director, Insurance Consultancy, Technology & Global Leadership, Pricing, Product, Claims & Underwriting, WTW, addressed in a recent report.

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Chapman warns that this new way of doing things could potentially go on to create “unfairness and even undermine the risk-pooling model that is fundamental to the industry” and that insurers need to be sensitive to ensure that they develop and use this technology “ethically and manage their customers’ data with watertight controls.”

Insurers Must Ensure They Develop And Use AI Ethically: WTW’s Chapman

He highlights that it is crucial to remember that AI does not reason, as algorithms have no ethics because they are just simply algorithms.

He says that instead of asking how ethical a firm’s AI is, “we should be asking how far ethics is taken into account by the people who design the AI, feed it data, and put it to use making decisions.”

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Furthermore, with machine learning continuing to generate value across the industry, Chapman states that the value of applying a clear ethical framework should be considered as an “essential component to successful adoption and value extraction.”

He highlights that key components in WTW’s own ethical framework, include accountability and fairness – understanding, measuring, and the mitigation of bias – of the models and systems in how they operate in practice, as well as understanding how they are built, and technical excellence to ensure models and systems are reliable and safe providing privacy and security by design.

Chapman concludes by noting that there is potential in AI within the industry, but for it to ultimately have the best impact, “it needs to have public trust.”


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