Abstract: Explainable AI (XAI) is a rapidly advancing area within artificial intelligence, dedicated to enhancing the transparency and interpretability of AI models. This field addresses a major obstacle to AI’s broader acceptance in critical domains like healthcare, finance, and legal services, where traditional AI systems often operate as "black boxes" with complex and opaque decision processes. XAI seeks to make these models more understandable, providing insights that are accessible to users. This paper delves into the significance of interpretability in AI, emphasizing how explainability fosters trust, accountability, and compliance with regulatory standards. Various techniques, including feature attribution, model distillation, and local interpretable model-agnostic explanations (LIME), are reviewed as tools to render AI decisions clearer and more reliable. By enhancing transparency, XAI not only aids in validating and debugging models but also tackles ethical issues related to bias and fairness within AI systems. This paper illustrates how XAI can bridge the divide between machine learning predictions and actionable insights, paving the way for AI systems that are both trustworthy and accountable. Through an analysis of current XAI methods and relevant case studies, the paper underscores XAI's potential to drive more ethically responsible and user-friendly AI solutions.

Keywords: Explainable AI (XAI), Interpretability, Transparency, Model distillation, Feature attribution, Local interpretable model-agnostic explanations (LIME), Trust in AI, Accountability, Regulatory compliance, Ethical AI

| DOI: 10.17148/IARJSET.2024.111026