AI, Ethics, and Insurance: Why Bias Begins at the Data Source
AI governance expert Sean Tumanov explores the ethical, legal, and practical risks of generative AI in insurance -from data privacy to model bias - and the critical importance of transparency, collaboration, and early testing in responsible model development.
Sean Tumanov, an AI governance leader with deep experience across banking and insurance, outlines the complex ethical and operational risks of deploying AI in financial services.
He emphasizes that responsible AI begins with understanding the origin and purpose of data, ensuring transparency and explainability, and proactively testing for bias - especially when models impact individuals.
With rising regulatory expectations and consumer trust at stake, he highlights the need for early-stage governance, clear use-case justification, and industry-wide collaboration to align on best practices and protect stakeholders.
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