How Insurers Can Navigate the Threat of Biased AI

With 90% of insurance companies having already implemented some form of AI or are in the process of doing so, many are benefitting from reduced costs, increased efficiencies and improved customer experience.

But AI comes with risk, especially with the data being used to train models. If biased data is used for decision-making, it leads to biased outputs – it can even amplify underlying bias. How does bias creep into AI models? Is bias always bad? Data plays a critical role in mitigating risk. There are three key steps insurers must take to understand how data causes bias in their AI’s outputs, and to protect themselves from any regulatory or reputational repercussions caused by flawed models.