Explainable Artificial Intelligence for Intelligent Transportation Systems

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the Intelligent Transportation Systems (ITS) field, but the lack of explainability in ML models can be problematic for safety-critical systems. Explainable AI (XAI) methods are proposed to address this issue, increasing public acceptance and trust in AI-based ITS. This book provides a comprehensive overview of XAI in ITS, including ethical, societal, and legal implications, and identifies future research directions and open problems.

Format: Hardback
Length: 276 pages
Publication date: 20 October 2023
Publisher: Taylor & Francis Ltd

Artificial Intelligence (AI) and Machine Learning (ML) are poised to transform various industries, including the Intelligent Transportation Systems (ITS) field. While ML, particularly deep learning models, have demonstrated remarkable accuracy in various tasks, their outcomes are often difficult for humans to comprehend and explain. This poses significant challenges, particularly for safety-critical systems such as transportation. To address this issue, Explainable AI (XAI) methods have been proposed to provide human-interpretable representations of machine learning models while maintaining performance. These methods have the potential to enhance public acceptance and trust in AI-based ITS.

This article provides a comprehensive overview of XAI methods in ITS applications, including their background, current state, ethical, societal, and legal implications, and future research directions. It serves as a valuable resource for newcomers to the field, as well as for interested practitioners, to gain a deeper understanding of XAI and its potential applications in the ITS domain.

Background:

AI and ML have gained significant attention in recent years due to their ability to solve complex problems and improve various aspects of our lives. AI refers to the ability of machines to perform tasks that would typically require human intelligence, while ML is a subset of AI that focuses on developing algorithms that can learn from data and make predictions.

ITS is a multidisciplinary field that aims to improve transportation systems through the use of advanced technologies. It encompasses various aspects such as traffic management, vehicle automation, and passenger experience. AI and ML have been applied to ITS to improve safety, efficiency, and sustainability.

Explainable AI:

XAI is a set of techniques that aim to make machine learning models more transparent and understandable to humans. It involves the development of visualizations, explanations, and interpretations that can help users understand the decision-making process of the model. XAI methods can be used to improve the trustworthiness of AI-based systems and to address ethical concerns related to AI.

Current State of XAI in ITS:

XAI methods have been applied to various ITS applications, including traffic management, vehicle automation, and passenger experience. Some of the most notable examples include:

Traffic Management: XAI methods have been used to improve traffic flow and reduce congestion. For example, machine learning algorithms can be used to predict traffic patterns and adjust traffic signals accordingly. XAI methods can also be used to identify traffic accidents and prevent them from occurring.

Vehicle Automation: XAI methods have been used to improve vehicle safety and efficiency. For example, machine learning algorithms can be used to detect obstacles and avoid collisions. XAI methods can also be used to optimize vehicle performance and reduce fuel consumption.

Passenger Experience: XAI methods have been used to improve passenger experience. For example, machine learning algorithms can be used to provide personalized recommendations to passengers based on their preferences and behavior. XAI methods can also be used to detect passenger emotions and provide appropriate support.

Ethical, Societal, and Legal Implications of XAI in ITS:

XAI has the potential to have significant ethical, societal, and legal implications in the context of ITS. Some of the most notable ethical concerns include:

Privacy: XAI methods may require access to personal data, which raises concerns about privacy. It is important to ensure that personal data is used responsibly and that individuals have control over their data.

Bias: Machine learning algorithms can be biased, which can lead to unfair outcomes. It is important to ensure that machine learning algorithms are trained on diverse data sets and that they are regularly updated to prevent bias.

Safety: XAI methods may be used to improve safety, but they can also introduce new risks. It is important to ensure that XAI methods are safe and that they are properly tested before they are deployed in real-world environments.

Societal Implications: XAI methods may have significant societal implications, such as job displacement and social inequality. It is important to ensure that XAI methods are developed in a way that benefits society as a whole.

Legal Implications: XAI methods may have legal implications, such as liability for accidents caused by AI-based systems. It is important to ensure that XAI methods are developed in a way that complies with legal regulations.

Future Research Directions:

XAI is a rapidly evolving field, and there are many open problems that need to be addressed. Some of the most notable future research directions include:

Interpretability: XAI methods should be able to provide more interpretable representations of machine learning models. This will help users understand the decision-making process of the model and make informed decisions.

Generalization: XAI methods should be able to generalize to new data sets and environments. This will help ensure that XAI methods are effective in real-world applications.

Ethical Considerations: XAI methods should be developed in a way that considers ethical concerns. This will help ensure that XAI methods are used in a responsible and ethical manner.

Societal Implications: XAI methods should be developed in a way that benefits society as a whole. This will help ensure that XAI methods are used in a way that promotes social equality and justice.

Legal Implications: XAI methods should be developed in a way that complies with legal regulations. This will help ensure that XAI methods are used in a way that is safe and responsible.

Conclusion:

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.

This article provides a comprehensive overview of XAI methods in ITS applications, including their background, current state, ethical, societal, and legal implications, and future research directions. It serves as a valuable resource for newcomers to the field, as well as for interested practitioners, to gain a deeper understanding of XAI and its potential applications in the ITS domain.

Weight: 690g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032344577