可解释的人工智能：重要性、使用领域、阶段、输出形状和挑战

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-11-23 DOI:10.1145/3705724

Naeem Ullah, Javed Ali Khan, Ivanoe De Falco, Giovanna Sannino

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引用次数: 0

摘要

许多应用领域迫切需要可解释的人工智能（XAI）方法，以增强人们对人工智能方法的信心和信任。目前的研究主要集中在 XAI 的特定方面，缺乏全面的视角。本研究对重要性、方式、方法和应用领域进行了系统调查，以弥补这一不足，并提供对 XAI 领域的全面了解。通过应用系统文献综述方法，我们找到并讨论了 155 篇论文，从而就 XAI 方法的优势、局限性和挑战以及未来研究方向展开了广泛讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges

There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence (XAI) approaches to boost people’s confidence and trust in Artificial Intelligence methods. Current works concentrate on specific aspects of XAI and avoid a comprehensive perspective. This study undertakes a systematic survey of importance, approaches, methods, and application domains to address this gap and provide a comprehensive understanding of the XAI domain. Applying the Systematic Literature Review approach has resulted in finding and discussing 155 papers, allowing a wide discussion on the strengths, limitations, and challenges of XAI methods and future research directions.

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.