Explainable AI and transparency in decision models

Hassan Qudrat-Ullah ()

Chapter 8 in Understanding Advanced Decision Making, 2025, pp 203-229 from Edward Elgar Publishing

Abstract: This chapter provides a comprehensive exploration of Explainable Artificial Intelligence (XAI), covering conceptual foundations, methodological approaches, and real-world applications. Intrinsic and post-hoc approaches are scrutinized, revealing the delicate balance between accuracy and interpretability crucial across diverse domains. The evolving legal landscape, compliance with standards and regulations, and the intricate interplay between legal obligations and ethical considerations are discussed, elucidating the multifaceted nature of transparency in AI. Ethical dimensions, user acceptance, and business implications are emphasized, underscoring the importance of addressing ethical concerns for responsible AI deployment. Real-world examples, spanning credit scoring models, medical diagnosis, and self-driving cars, illustrate the practical significance of transparent decision models. The chapter addresses implications and limitations, acknowledging potential benefits, like enhanced public trust, while recognizing challenges such as trade-offs, computational complexities, and biases. Future research directions propose opportunities for refinement, focusing on advancing methods, exploring deep learning interpretability, and aligning practices with evolving regulations.

Keywords: Explainable Artificial Intelligence; XAI; Transparency; Interpretability; Standards and regulations; Ethical considerations; User acceptance; Business implications; Real-world applications (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035346233
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