Explainable Artificial Intelligence (XAI) methods help to understand the
internal mechanism of machine learning models and how they reach a specific
decision or made a specific action. The list of informative features is one of
the most common output of XAI methods. Multicollinearity is one of the big
issue that should be considered when XAI generates the explanation in terms of
the most informative features in an AI system. No review has been dedicated to
investigate the current approaches to handle such significant issue. In this
paper, we provide a review of the current state-of-the-art approaches in
relation to the XAI in the context of recent advances in dealing with the
multicollinearity issue. To do so, we searched in three repositories that are:
Web of Science, Scopus and IEEE Xplore to find pertinent published papers.
After excluding irrelevant papers, seven papers were considered in the review.
In addition, we discuss the current XAI methods and their limitations in
dealing with the multicollinearity and suggest future directions.

通过调查现有方法处理多重共线性问题，本文提供了在最新的处理多重共线性问题的背景下，解释性人工智能的最新技术方法的综述，并讨论了当前解释性人工智能方法及其在处理多重共线性问题方面的局限性，并提出未来的研究方向。