Strategies for applying interpretable and explainable AI in real world IoT applications

With the growing complexity of AI models, and as well as the requirement for transparency and trust, integrating XAI into IoT applications is gaining greater momentum. Most traditional AI models are regarded as "black boxes," in which understanding decision-making processes presents a challenge in crucial domains such as healthcare, smart cities, and industrial automation. This paper looks at various methodologies of XAI that can be used to increase interpretability and offer better insight into the AI models of IoT environments. This research follows a taxonomy-based approach in the classification of ante-hoc and post-hoc XAI techniques, with the main focus on model-agnostic methods such as tree Explainer SHAP and LIME. Various real-world use cases are analysed for the effectiveness of XAI in enhancing trust, reducing biases, and improving the accuracy of decision-making in IoT systems. Challenges associated with the trade-off between model complexity and interpretability while maintaining efficiency are discussed. The results indicated that XAI techniques could significantly enhance the transparency and trust of IoT applications on account of clear and interpretable explanations of AI-driven decisions. These findings have been considered most effective in domains where ethical compliance and accountability are considered paramount. The study also points to the contribution of XAI in optimizing energy efficiency and improving data security in IoT frameworks. The integration of XAI into IoT systems bridges the gap in transparency and opens ways for scalable, trustworthy, and ethical AI applications. Of course, scalability challenges have to be overcome, and further security mechanisms have to be developed in future research to realize the full potential of XAI in IoT domains.

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