Robust Explanations: The Case of Prime Implicants

Chenrui Zhu, Vu-Linh Nguyen, Marie-Hélène Masson, Sébastien Destercke

Abstract: This paper studies the problem of quantifying the robustness level of explanations. In particular, we propose to quantify the robustness level of each given explanation as the maximum level of perturbation on the parameters of a classifier below which an explanation remains valid. We derive theoretical results on the computational complexity of determining the robustness level in the case of linear models, which underpin the design of a log-linear time algorithm. We then apply the proposed notion of robustness level to analyze the robustness of commonly used specific types of prime implicants, including the shortest and the most robust ones. The insights are then leveraged to construct guidelines on scenarios where each type of explanations may be more beneficial.

External IDs:dblp:conf/ecsqaru/ZhuNMD25