BRIDGING THE INTERPRETABILITY GAP: EXPLORING EXPLAINABLE AI IN DATA ANALYTICS

Abstract

This research paper explores the crucial nexus between data analytics and artificial intelligence (AI), with an emphasis on Explainable AI (XAI) as a means of overcoming the interpretability gap. In a time when sophisticated AI models frequently operate as "black boxes," it is critical to comprehend how they make decisions. The paper addresses the challenge of achieving a balance between the accuracy of AI models and the interpretability required for stakeholders to trust and comprehend the insights derived from data analytics. The study covers a range of approaches and techniques utilized in Explainable AI, providing insight into how complex AI models can incorporate interpretability. By analyzing the evolving landscape of explainability techniques, the paper evaluates their effectiveness in enhancing the transparency and interpretability of AI-driven data analytics. Additionally, the ethical implications of using AI models are examined, with a focus on the significance of openness, responsibility, and user confidence. The work lays the groundwork for future developments in the field by offering insights into the changing field of Explainable AI and its function in resolving interpretability issues. In summary, the study highlights the importance of Explainable AI as a link that guarantees precise outcomes while also promoting a more profound comprehension of the processes involved in making decisions in the field of data analytics.