Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization. (arXiv:2205.10232v1 [cs.LG])

There is a broad consensus on the importance of deep learning models in tasks
involving complex data. Often, an adequate understanding of these models is
required when focusing on the transparency of decisions in human-critical
applications. Besides other explainability techniques, trustworthiness can be
achieved by using counterfactuals, like the way a human becomes familiar with
an unknown process: by understanding the hypothetical circumstances under which
the output changes. In this work we argue that automated counterfactual
generation should regard several …

arxiv change optimization power trade