Explainable Artificial Intelligence (XAI) has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this additional information to end users. In this article, we conduct a scoping review of existing literature to elicit details on the conflict between privacy and explainability. Using the standard methodology for scoping review, we extracted 57 articles from 1,943 studies published from January 2019 to December 2024. The review addresses 3 research questions to present readers with more understanding of the topic: (1) what are the privacy risks of releasing explanations in AI systems? (2) what current methods have researchers employed to achieve privacy preservation in XAI systems? (3) what constitutes a privacy preserving explanation? Based on the knowledge synthesized from the selected studies, we categorize the privacy risks and preservation methods in XAI and propose the characteristics of privacy preserving explanations to aid researchers and practitioners in understanding the requirements of XAI that is privacy compliant. Lastly, we identify the challenges in balancing privacy with other system desiderata and provide recommendations for achieving privacy preserving XAI. We expect that this review will shed light on the complex relationship of privacy and explainability, both being the fundamental principles of Trustworthy AI.

翻译：可解释人工智能（XAI）已成为可信人工智能的支柱，旨在为本质不透明的复杂模型提供透明度。尽管在模型中融入解释具有诸多益处，但向终端用户提供此类额外信息所引发的隐私问题亟待解决。本文通过对现有文献进行范围综述，以揭示隐私与可解释性之间的冲突细节。采用标准的范围综述方法，我们从2019年1月至2024年12月发表的1,943项研究中筛选出57篇文献。本综述通过回答三个研究问题，帮助读者更深入理解该主题：（1）在AI系统中发布解释存在哪些隐私风险？（2）研究者目前已采用哪些方法实现XAI系统的隐私保护？（3）隐私保护型解释应具备哪些特征？基于所选文献的综合分析，我们对XAI中的隐私风险与保护方法进行了分类，并提出了隐私保护型解释的特征框架，以助力研究者和实践者理解符合隐私合规要求的XAI系统构建要素。最后，我们指出了在隐私与其他系统需求之间取得平衡所面临的挑战，并为实现隐私保护型XAI提出建议。我们期望本综述能阐明隐私与可解释性——二者同为可信人工智能基本原则——之间的复杂关系。