The level of strength of an explanation: A quantitative evaluation technique for post-hoc XAI methods
Marilyn Bello, R Amador, María-Matilde García, Javier Del Ser, Pablo Mesejo, Óscar Cordón
Abstract
Explainability has become one of the leading research topics within Artificial Intelligence (AI) in the last few years, as it has increased the confidence and credibility of “black box” models, such as deep neural networks . However, the evaluation of the explanations provided by different explainability approaches remains a hot research topic. This evaluation enables the possibility of comparing different existing techniques and would play a crucial role in improving the auditability of AI-based systems. The current literature on the subject considers that the evaluation of explainability methods can be approached in two ways: qualitative and quantitative . While qualitative evaluations are based on assumptions induced by human understanding that are hard to develop and prone to introduce certain cognitive biases, quantitative ones avoid these biases by excluding the human expert from the evaluation process. However, the main challenge in quantitatively evaluating an explanation is the lack of ground truth specifying what defines a correct explanation. In this paper, we propose an evaluation measure that quantifies the Level of Strength of an Explanation (LSE), i.e., the extent to which the explanation produced by a post-hoc explainability method supports the class predicted by a classifier. Our proposal is inspired by the semantics underlying the Likelihood Ratio in evaluating forensic evidence, which is defined as the weight to be attributed to a piece of forensic evidence according to the prosecution and defense propositions. To validate our proposal, nine popular explainability techniques are compared across two deep neural architectures dedicated to image classification and three classifiers for binary classification over tabular datasets. In addition, we use MetaQuantus as a meta-evaluation approach. Results from our experimental study reveal that GradCAM and LRP outperform the other explainability methods in terms of the proposed LSE measure.