Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that have identified challenges and potential research directions of XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey of challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions of XAI and (2) challenges and research directions of XAI based on machine learning life cycle's phases: design, development, and deployment. We believe that our meta-survey contributes to XAI literature by providing a guide for future exploration in the XAI area .(c) 2023 The Author(s). Published by Elsevier B.V.

Automated summary

Significant progress has been made in artificial intelligence (AI) in the past decade, but the increasing complexity and lack of transparency of black-box AI models remain challenges. To address this, Explainable AI (XAI) has been proposed to make AI more transparent and advance its adoption. This study provides a systematic meta-survey on the challenges and future research directions in XAI, organized into general challenges and research directions, as well as challenges and research directions based on the machine learning life cycle's phases: design, development, and deployment. It contributes to the XAI literature by offering a guide for future exploration.