Xi–Lorenz eXplainable Artificial Intelligence

ABSTRACT A crucial issue for the trustworthiness of Artificial Intelligence methods is to ensure that their results are explainable to human beings. The most employed model agnostic measures to assess explainability are Shapley‐values whose application, however, suffers from being computationally intensive. In this paper we overcome this drawback through a new explainable AI tool: the Xi–Lorenz method, which can provide both pre hoc (pre‐model) and post hoc (post‐model) explanations. The advantage of doing so is that we can separate input evaluation (in the pre hoc stage) from output evaluation (in the post hoc stage), enabling to distinguish the influence of data quality from the reasoning capacity of the model. By assessing inputs independently from outputs, one can determine whether observed shortcomings are primarily attributable to noisy, incomplete, or biased data, or whether they reflect limitations in the model's internal mechanisms. We demonstrate the practical advantages of the Xi–Lorenz method resorting to both simulated and real data. Our findings indicate that the proposed method is effective and that it improves the extant explainable AI methods.