"Even if ..." -- Diverse Semifactual Explanations of Reject
This work addresses the need for interpretability in safety-critical ML systems with reject options, offering a novel explanation method for a largely unexplored area.
The authors tackled the problem of explaining why machine learning systems reject uncertain inputs by proposing semifactual explanations, a novel approach in explainable AI, and demonstrated its effectiveness on a conformal prediction-based reject option.
Machine learning based decision making systems applied in safety critical areas require reliable high certainty predictions. For this purpose, the system can be extended by an reject option which allows the system to reject inputs where only a prediction with an unacceptably low certainty would be possible. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected. With the ongoing rise of eXplainable AI (XAI), a lot of explanation methodologies for machine learning based systems have been developed -- explaining reject options, however, is still a novel field where only very little prior work exists. In this work, we propose to explain rejects by semifactual explanations, an instance of example-based explanation methods, which them self have not been widely considered in the XAI community yet. We propose a conceptual modeling of semifactual explanations for arbitrary reject options and empirically evaluate a specific implementation on a conformal prediction based reject option.