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Bias can arise at multiple stages of actuarial data-driven decision making, from data collection and modeling to interpretation and implementation. In this webinar, we consider how different types of bias, such as measurement bias, sampling bias, algorithmic bias, and historical bias, may affect actuarial applications. These biases can distort statistical estimates, undermine predictive performance, or lead to unfair and discriminatory outcomes in insurance pricing, reserving, and risk management. While some forms of statistical bias may be intentionally introduced to improve generalization (e.g., through the bias–variance trade-off), fairness-related biases are more problematic, as they can erode trust, compliance, and social responsibility. This session will leverage academic research to explore real-world actuarial contexts where bias may emerge and to discuss strategies for identifying and mitigating undesirable bias to support more accurate, transparent, and equitable decision-making.