EXPLORING THE LANDSCAPE OF EXPLAINABLE ARTIFICIAL INTELLIGENCE: BENEFITS, CHALLENGES, AND FUTURE PERSPECTIVES
Abstract
This research paper delves into the dynamic realm of Explainable Artificial Intelligence (XAI), scrutinizing its advantages and limitations. XAI emerges as a pivotal facet in the evolution of artificial intelligence (AI) systems, emphasizing transparency to render AI systems comprehensible to humans. The primary objective of XAI is to illuminate the decision-making processes of complex AI models, offering insights into their reasoning mechanisms. Through heightened transparency, XAI aims to enhance human comprehension, instill trust in AI outcomes, and ultimately foster accountability, ethical adherence, and user confidence in AI systems. This paper presents a comprehensive analysis of the benefits of XAI, explores its constraints concerning individual privacy, and discusses the future perspectives of this rapidly evolving field.
How to Cite This Article
Abhinav Agarwal (2023); EXPLORING THE LANDSCAPE OF EXPLAINABLE ARTIFICIAL INTELLIGENCE: BENEFITS, CHALLENGES, AND FUTURE PERSPECTIVES, International Journal of Advanced Research (IJAR), 11 (12), 1042-1046, ISSN 2320-5407. DOI: https://doi.org/10.21474/IJAR01/18074
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