Explainable AI for Intelligent transportation systems /

"Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amena...

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Bibliographic Details
Corporate Author: Taylor & Francis
Other Authors: Adadi, Amina (Editor), Bouhoute, Afaf (Editor)
Format: eBook
Language:English
Published: Boca Raton : CRC Press, 2024.
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary:"Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can be hardly explained. This can be very problematic especially for systems of a safety-critical nature such as transportation systems. Explainable AI methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. Examining explainable AI in the field of ITS, this book has the following key features: provides the necessary background for newcomers to the field (both academics and interested partitioners). presents a timely snapshot of explainable and interpretable models in ITS applications. discusses ethical, societal, and legal implications of adopting XAI in the context of ITS. identifies future research directions and open problems"--
Physical Description:1 online resource
Bibliography:Includes bibliographical references and index.
ISBN:9781003324140
1003324142