Explainable AI: Examining Challenges and Opportunities in Developing Explainable AI Systems for Transparent Decision-Making
Keywords:
Explainable AI, XAI, transparent decision-making, interpretability, accountability, model complexity, ethical considerations, model trustworthiness, regulatory complianceAbstract
Explainable AI (XAI) has emerged as a critical area of research to address the opacity of complex machine learning models. This paper explores the challenges and opportunities in developing XAI systems for transparent decision-making. We discuss the importance of XAI in various domains, including healthcare, finance, and autonomous systems, and highlight the need for interpretability, accountability, and fairness in AI. We analyze the challenges of implementing XAI, such as model complexity, interpretability-accuracy trade-offs, and ethical considerations. Additionally, we examine the opportunities that XAI presents, including improved model trustworthiness, user understanding, and regulatory compliance. We also discuss future directions for XAI research and its potential impact on society.
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