Deciphering AI Decisions: a Closer Look at Explainable Artificial Intelligence

EasyChair Preprint 12309

Abstract

Artificial Intelligence (AI) systems have become increasingly integrated into various aspects of our lives, influencing decisions in critical domains such as finance, healthcare, and criminal justice. However, as these systems grow more complex, understanding their decision-making processes becomes increasingly challenging. Explainable Artificial Intelligence (XAI) has emerged as a critical field aiming to bridge this gap by providing transparency and interpretability in AI systems' decisions. In this paper, we delve into the concept of XAI and explore its significance in enhancing trust, accountability, and fairness in AI-driven decision-making. We examine different approaches and techniques within the realm of XAI, ranging from model-agnostic methods to interpretable models specifically designed to provide insights into AI reasoning. Additionally, we discuss the challenges and limitations associated with XAI, including the trade-off between transparency and performance, as well as the potential biases inherent in human interpretation. Furthermore, we highlight the practical applications of XAI across various industries and contexts, illustrating how it can empower end-users, domain experts, and policymakers to better understand, validate, and ultimately trust AI-driven decisions. Through real-world examples and case studies, we showcase the transformative potential of XAI in fostering responsible AI deployment and mitigating the risks of unintended consequences.

Keyphrases: Artificial Intelligence, Explainable Artificial Intelligence, transparency

@booklet{EasyChair:12309,
  author    = {James Henry and Thomas Martin},
  title     = {Deciphering AI Decisions: a Closer Look at Explainable Artificial Intelligence},
  howpublished = {EasyChair Preprint 12309},
  year      = {EasyChair, 2024}}