Magazine

Why Explainability is Not the Problem in Machine Learning Risk

Joe Breeden challenges the widespread focus on explainability in AI risk management, arguing that the real issue lies in predictive uncertainty. Explainability is only valuable when model outputs are reliable, yet most institutions fail to measure confidence in predictions. This can lead to misplaced trust in both outputs and explanations. The article advocates for a shift toward embedding uncertainty into decision-making frameworks and implementing fallback mechanisms when predictions are weak. Ultimately, effective risk management depends on understanding when models should be trusted, not just how they work.