Conventional explainability often centers on predictions, but a shift towards understanding uncertainty estimates is gaining traction. This approach emphasizes the importance of not just what a model predicts, but how confident it is in those predictions. By focusing on uncertainty, new insights into AI decision-making processes can emerge.
