Bridging the Gap: Making AI Understandable with Explainable Artificial Intelligence

EasyChair Preprint 12310

Abstract

Artificial Intelligence (AI) has rapidly evolved, penetrating various facets of modern life, from healthcare to finance, and autonomous vehicles to personal assistants. While AI promises remarkable advancements, its black-box nature often leads to skepticism, fear, and mistrust among users and stakeholders. Explainable Artificial Intelligence (XAI) emerges as a pivotal approach to address these concerns by enhancing transparency and interpretability in AI systems. This paper explores the significance of XAI in bridging the gap between AI systems and end-users. We delve into the fundamental concepts and methodologies behind XAI, shedding light on techniques such as rule-based models, interpretable machine learning algorithms, and post-hoc explanation methods. By providing comprehensible explanations of AI decisions, XAI empowers users to trust, verify, and potentially correct AI outcomes, fostering collaboration and synergy between humans and machines. Moreover, we discuss the diverse applications of XAI across industries, including healthcare, finance, and autonomous systems, illustrating how transparent AI systems can enhance decision-making, accountability, and fairness. Furthermore, we examine the ethical implications and challenges associated with implementing XAI, emphasizing the importance of balancing transparency with privacy, security, and performance.

Keyphrases: Artificial Intelligence, Explainable Artificial Intelligence, transparency

@booklet{EasyChair:12310,
  author    = {James Henry and Serkan Habib},
  title     = {Bridging the Gap: Making AI Understandable with Explainable Artificial Intelligence},
  howpublished = {EasyChair Preprint 12310},
  year      = {EasyChair, 2024}}