Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities

TL;DR

This paper provides a comprehensive meta-survey of current challenges and future opportunities in Explainable AI, organizing insights across general themes and specific phases of the machine learning lifecycle to guide future research.

Contribution

It offers a systematic overview of challenges and research directions in XAI, consolidating scattered literature into a structured guide for future exploration.

Findings

Identifies key challenges in XAI development and deployment.

Highlights research gaps across the machine learning lifecycle.

Suggests future research directions for enhancing XAI transparency.

Abstract

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 identified challenges and potential research directions in XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey for challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions in XAI and (2) challenges and research directions in 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.