Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations. (arXiv:2106.12723v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2106.12723

Understanding and explaining the mistakes made by trained models is critical
to many machine learning objectives, such as improving robustness, addressing
concept drift, and mitigating biases. However, this is often an ad hoc process
that involves manually looking at the model's mistakes on many test samples and
guessing at the underlying reasons for those incorrect predictions. In this
paper, we propose a systematic approach, conceptual counterfactual explanations
(CCE), that explains why a classifier makes a mistake on a particular test …

arxiv debugging lg mistakes model