As artificial intelligence continues to shape decision-making in insurance, healthcare, and risk management, concerns about bias in AI models are growing. Actuaries rely on data-driven insights to assess risk, but what happens when the underlying data and algorithms introduce unintended discrimination? This session explores the realities of AI bias, from flawed training datasets to opaque algorithms, and examines its impact on actuarial models, underwriting, and decision-making. Participants will gain insights into: Real-world case studies of AI bias and their implications for actuarial work.>How biases emerge in AI-driven models, including recruitment, risk assessment, and financial predictions.>Regulatory and ethical considerations shaping AI fairness in insurance and finance.>Practical strategies for actuaries to detect, mitigate, and advocate against AI bias in their organizations. This session provides a balanced discussion of challenges and solutions, equipping actuaries with the knowledge to navigate AI developments responsibly while maintaining fairness and integrity in their profession.