While deep learning algorithms can excel at predicting outcomes, they often act as black-boxes rendering them uninterpretable for healthcare practitioners. Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning models make particular decisions. We introduce a novel algorithm that leverages uncertainty to generate trustworthy counterfactual explanations for white-box models. Our proposed method can generate more interpretable CEs than the current benchmark (Van Looveren and Klaise, 2019) for breast cancer diagnosis. Further, our approach provides confidence levels for both the diagnosis as well as the explanation.
Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal
ML4H: Machine Learning for Health Workshop NeurIPS, 2020