With the rise of complex cyber devices Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making sense requires new techniques, such as from the field of Artificial Intelligence (AI). To apply these techniques successfully in CF, we need to justify and explain the results to the stakeholders of CF, such as forensic analysts and members of the court, for them to make an informed decision. If we want to apply AI successfully in CF, there is a need to develop trust in AI systems. Some other factors in accepting the use of AI in CF are to make AI authentic, interpretable, understandable, and interactive. This way, AI systems will be more acceptable to the public and ensure alignment with legal standards. An explainable AI (XAI) system can play this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and is still in its infancy. In this paper, we explore and make a case for the significance and advantages of XAI-CF. We strongly emphasize the need to build a successful and practical XAI-CF system and discuss some of the main requirements and prerequisites of such a system. We present a formal definition of the terms CF and XAI-CF and a comprehensive literature review of previous works that apply and utilize XAI to build and increase trust in CF. We discuss some challenges facing XAI-CF. We also provide some concrete solutions to these challenges. We identify key insights and future research directions for building XAI applications for CF. This paper is an effort to explore and familiarize the readers with the role of XAI applications in CF, and we believe that our work provides a promising basis for future researchers interested in XAI-CF.

翻译：随着复杂数字设备的兴起，数字取证（CF）正面临着诸多新挑战。例如，智能手机上运行着数十个系统，每个系统拥有数百万可下载的应用程序。筛选如此海量的数据并从中提取有效信息，需要借助人工智能（AI）等新技术。为了在CF中成功应用这些技术，我们必须向CF的利益相关者（如取证分析师和法庭成员）证明并解释结果，以便他们做出明智的决策。若要在CF中成功应用AI，则需建立对AI系统的信任。影响AI在CF中接受度的其他因素包括：使AI具有真实性、可解释性、可理解性和交互性。这样，AI系统将更容易被公众接受，并确保符合法律标准。可解释人工智能（XAI）系统可在CF中发挥此作用，我们将其称为XAI-CF系统。XAI-CF不可或缺，但仍处于起步阶段。本文探讨并论证了XAI-CF的重要性和优势。我们强烈主张构建成功且实用的XAI-CF系统，并讨论了此类系统的主要要求和先决条件。我们给出了CF与XAI-CF的形式化定义，并对先前利用XAI构建并增强CF信任度的研究进行了全面的文献综述。我们探讨了XAI-CF面临的一些挑战，并提出了具体的解决方案。我们识别了为CF构建XAI应用的关键见解与未来研究方向。本文旨在探索并让读者熟悉XAI在CF中的应用角色，我们相信这项工作为未来对XAI-CF感兴趣的研究者提供了有前景的基础。