To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
This addresses the problem of unreliable explanations in XAI for researchers and end users, offering a standardized evaluation tool, though it is incremental as it builds on existing metrics and focuses on a specific class of methods.
The paper tackles the lack of standardized evaluation for local linear XAI methods like LIME and SHAP, which suffer from defects such as unstable explanations and divergence from theory, by introducing LEAF, an open Python framework that provides a clear set of metrics for unbiased assessment.
The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective explanations for black-box classifiers. The existing literature lists many desirable properties for explanations to be useful, but there is no consensus on how to quantitatively evaluate explanations in practice. Moreover, explanations are typically used only to inspect black-box models, and the proactive use of explanations as a decision support is generally overlooked. Among the many approaches to XAI, a widely adopted paradigm is Local Linear Explanations - with LIME and SHAP emerging as state-of-the-art methods. We show that these methods are plagued by many defects including unstable explanations, divergence of actual implementations from the promised theoretical properties, and explanations for the wrong label. This highlights the need to have standard and unbiased evaluation procedures for Local Linear Explanations in the XAI field. In this paper we address the problem of identifying a clear and unambiguous set of metrics for the evaluation of Local Linear Explanations. This set includes both existing and novel metrics defined specifically for this class of explanations. All metrics have been included in an open Python framework, named LEAF. The purpose of LEAF is to provide a reference for end users to evaluate explanations in a standardised and unbiased way, and to guide researchers towards developing improved explainable techniques.