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Image of - Explainable Artificial Intelligence For Intelligent Transportation Systems | Hardcover
Explainable Artificial Intelligence For Intelligent Transportation Systems | Hardcover

Explainable Artificial Intelligence For Intelligent Transportation Systems | Hardcover

by Adadi A.

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 hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) 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.FEATURES:Provides the necessary background for newcomers to the field (both academics and interested practitioners)Presents a timely snapshot of explainable and interpretable models in ITS applicationsDiscusses ethical, societal, and legal implications of adopting XAI in the context of ITSIdentifies future research directions and open problems

Highlights

  • binding-icon

    9781032344577

    ISBN:

  • binding-icon

    Adadi A.

    Author:

  • binding-icon

    276

    Pages:

  • binding-icon

    6100 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2024

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹9220

₹11525

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 hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) 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.FEATURES:Provides the necessary background for newcomers to the field (both academics and interested practitioners)Presents a timely snapshot of explainable and interpretable models in ITS applicationsDiscusses ethical, societal, and legal implications of adopting XAI in the context of ITSIdentifies future research directions and open problems

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