Explaining Reject Options of Learning Vector Quantization Classifiers

Reject options are an underexplored but important feature for responsible AI: The paper identifies that while machine learning models can be designed to "reject" uncertain predictions rather than guess, explanations for why a model rejected an input have been largely ignored in the explainability literature—a gap that matters for user trust and accountability.

Counterfactual explanations can clarify rejection decisions: The researchers propose using counterfactual explanations (showing what would need to change for a model to accept an input) as a practical way to help users understand why their input was rejected, making model behavior more transparent.

Prototype-based classifiers offer interpretability advantages: The study focuses on learning vector quantization models, which use representative examples ("prototypes") to make decisions—these models are inherently more interpretable than black-box alternatives and provide a natural foundation for explaining both predictions and rejections.

Efficiency matters for real-world deployment: The paper addresses how to compute these counterfactual explanations efficiently for reject options, suggesting the authors recognize that explainability methods must be practical enough to implement in actual systems, not just theoretically sound.