AI-enabled IoT Applications: Towards a Transparent Governance Framework
Nadine Y. Fares, Denis Nedeljkovic, Manar Jammal
Abstract
Internet of Things (IoT) and Artificial Intelligence (AI) systems have become prevalent across various industries, steering to diverse and far-reaching outcomes, and their convergence has garnered significant attention in the tech world. Studies and reviews are instrumental in supplying industries with the nuanced understanding of the multifaceted developments of this joint domain. This paper undertakes a critical examination of existing perspectives and governance policies, adopting a contextual approach, and addressing not only the potential but also the limitations of these governance policies. In the complex landscape of AI-infused IoT systems, transparency and interpretability are pivotal qualities for informed decision-making and effective governance. In AI governance, transparency allows for scrutiny and accountability, while interpretability facilitates trust and confidence in AI-driven decisions. Therefore, we also evaluate and advocate for the use of two very popular eXplainable AI (XAI) techniques-SHAP and LIME-in explaining the predictive results of AI models. Subsequently, this paper underscores the imperative of not only maximizing the advantages and services derived from the incorporation of IoT and AI but also diligently minimizing possible risks and challenges.