Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey
It tackles the problem of trust and comprehension in AI for IoT applications, but it is incremental as it synthesizes existing work rather than proposing new methods.
This survey paper reviews the integration of Explainable Artificial Intelligence (XAI) models within the Internet of Things (IoT) domain, addressing the lack of interpretability in black-box AI models by categorizing studies and identifying open issues for future research.
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.