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2021 | OriginalPaper | Chapter

The Methods and Approaches of Explainable Artificial Intelligence

Authors : Mateusz Szczepański, Michał Choraś, Marek Pawlicki, Aleksandra Pawlicka

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

The chapter delves into the necessity and methods of Explainable Artificial Intelligence (xAI), emphasizing the importance of understanding AI decision-making processes. It discusses the challenges posed by black-box AI models and the need for transparency, especially in high-stakes decision-making scenarios. The text explores various xAI taxonomies and solutions, including inherently explainable models and post-hoc explanation methods. It also highlights practical implementations of xAI in fields like finance and cybersecurity, showcasing the potential benefits of explainable AI. The chapter concludes by emphasizing the importance of xAI in ensuring reliability, trust, and fairness in AI systems, making it a compelling read for professionals seeking to understand and implement explainability in AI.
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Metadata
Title
The Methods and Approaches of Explainable Artificial Intelligence
Authors
Mateusz Szczepański
Michał Choraś
Marek Pawlicki
Aleksandra Pawlicka
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77970-2_1

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