Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the results are critical, such black-box AI models are not sufficient. Explainable Artificial Intelligence (XAI) addresses this problem and defines a set of AI models that are interpretable by the users. Recently, several number of XAI models have been to address the issues surrounding by lack of interpretability and explainability of black-box models in various application areas such as healthcare, military, energy, financial and industrial domains. Although the concept of XAI has gained great deal of attention recently, its integration into the IoT domain has not yet been fully defined. In this paper, we provide an in-depth and systematic review of recent studies using XAI models in the scope of IoT domain. We categorize the studies according to their methodology and applications areas. In addition, we aim to focus on the challenging problems and open issues and give future directions to guide the developers and researchers for prospective future investigations.
翻译:人工智能(AI)模型的黑匣子性质不允许用户理解和有时信任这种模型产生的产出。在AI应用中,不仅结果至关重要,而且结果的决策路径也至关重要。在AI应用中,这种黑盒子AI模型是不够的。可以解释的人工智能(XAI)模型解决这一问题,并界定了一套用户可以解释的AI模型。最近,一些XAI模型是为了解决在保健、军事、能源、金融和工业等不同应用领域缺乏黑盒模型的可解释性和可解释性引起的问题。虽然XAI的概念最近引起了很大的注意,但尚未充分界定它与IOT域的结合。在本文件中,我们对最近使用IOT域范围内的XAI模型进行的研究进行了深入和系统的审查。我们根据这些研究的方法和应用领域对研究进行分类。此外,我们的目标是集中研究具有挑战性的问题和开放性问题,并在今后指导开发者和研究人员未来调查。