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Artificial intelligence (AI) and machine learning (ML) are widely employed to make the solutions more accurate and autonomous in many smart and intelligent applications in the Internet of Things (IoT). In these IoT applications, the performance and accuracy of AI/ML models are the main concerns; however, the transparency, interpretability, and responsibility of the models’ decisions are often neglected. Moreover, in AI/ML-supported next-generation IoT applications, there is a need for more reliable, transparent, and explainable systems. In particular, regardless of whether the decisions are simple or complex, how the decision is made, which features affect the decision, and their adoption and interpretation by people or experts are crucial issues. Also, people typically perceive unpredictable or opaque AI outcomes with skepticism, which reduces the adoption and proliferation of IoT applications. To that end …