Demystifying Explainable Artificial Intelligence: a Comprehensive Guide

EasyChair Preprint no. 12308

Abstract

Artificial Intelligence (AI) systems are increasingly pervasive in our daily lives, impacting decisions ranging from loan approvals to medical diagnoses. However, the opacity of many AI models raises concerns about bias, fairness, and trustworthiness. Explainable AI (XAI) aims to address these concerns by providing insights into how AI systems make decisions, enabling users to understand, trust, and ultimately, improve these systems. This comprehensive guide demystifies Explainable Artificial Intelligence (XAI) by elucidating its key concepts, methodologies, and applications. Beginning with an overview of the importance and challenges of XAI, we delve into various techniques used for explainability, including rule-based models, model-agnostic methods, and post hoc interpretation techniques. We discuss the trade-offs between interpretability and performance, highlighting the need for balancing transparency with accuracy. Furthermore, we explore real-world applications of XAI across diverse domains, such as healthcare, finance, and criminal justice. By examining case studies and best practices, we illustrate how XAI can enhance decision-making processes, mitigate biases, and foster accountability. Moreover, this guide addresses the ethical and societal implications of XAI, including privacy concerns, algorithmic fairness, and regulatory considerations. We advocate for the responsible development and deployment of AI systems, emphasizing the importance of transparency, accountability, and user empowerment.

Keyphrases: Explainable Artificial Intelligence (XAI), interpretability, transparency

@Booklet{EasyChair:12308,
  author = {James Henry and Hamid Obaid},
  title = {Demystifying Explainable Artificial Intelligence: a Comprehensive Guide},
  howpublished = {EasyChair Preprint no. 12308},

  year = {EasyChair, 2024}}