Artificial intelligence is rapidly transforming the insurance and financial services industries, offering new levels of efficiency, automation, and predictive power. But with these advancements comes a growing concern—bias. When AI systems are trained on historical data that reflects past inequalities or incomplete information, they risk reinforcing those same biases in decisions such as underwriting, claims processing, and fraud detection.

In this episode of InsurTech Amplified, Ichun Lai, Principal at Propel Global Advisory, and Theresa Blissing, best-selling author and Founder of InsurTech Amplified, explore how artificial intelligence can both solve and amplify long-standing issues in the insurance industry—particularly bias. They explain how AI models, when trained on historical or incomplete data, risk replicating societal inequalities at scale.

Bias isn’t just a technical flaw; it’s a product of decisions around problem framing, feature selection, and human oversight. Whether AI is fully autonomous or human-assisted, every point in the decision-making spectrum carries the potential for bias, making awareness and vigilance essential across the organization.

As AI becomes more embedded in our lives, Theresa and Ichun urge organizations to design with intention—placing human values at the core of every algorithm to build a more inclusive, trustworthy, and responsible insurance industry.

Theresa and Ichun worked with some amazing industry professionals to produce a white paper about bias in AI-driven decision making which you can read here.