When a financial AI system showed perfect fairness metrics across all demographics, its creators were proud. But examining the weekend data revealed an uncomfortable truth: the system was 40% less likely to approve transactions outside traditional banking hours, inadvertently encoding socioeconomic bias into its “fair” decisions. After analyzing hundreds of AI systems throughout 2024, I’ve discovered that the most sophisticated approaches to fairness often create the most insidious biases. Join me as we explore the hidden complexities of AI fairness and uncover practical strategies for building AI systems that truly work for everyone. Drawing from real-world implementations and hard-learned lessons, we’ll examine why perfect metrics often hide deeper problems, and how organizations can move beyond surface-level equality to achieve genuine equity in their AI systems. […]
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