Explainable AI for Cloud-Based Machine Learning Interpretable Models and Transparency in Decision Making.

Abstract

As machine learning models become increasingly complex and ubiquitous in cloud-based applications, the need for interpretability and transparency in decision making has become paramount. Explainable AI (XAI) techniques aim to provide insights into the inner workings of machine learning models, thereby enhancing their interpretability and facilitating trust among users. In this paper, we delve into the significance of XAI in cloud-based machine learning environments, emphasizing the importance of interpretable models and transparent decision-making processes. [1] XAI epitomizes a paradigm shift in cloud-based ML, catalyzing transparency, accountability, and ethical decision-making. As cloud-based ML continues its ascent, the imperative for XAI grows commensurately, underlining the necessity for sustained innovation and collaboration to unlock the full potential of interpretable AI systems. We review existing methodologies for achieving explainability in AI systems and discuss their applicability and challenges in cloud environments. Furthermore, we explore the implications of XAI for various stakeholders, including developers, end-users, and regulatory bodies, and highlight potential avenues for future research in this rapidly evolving field. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6376