Explainable Artificial Intelligence for Intelligent Transportation Systems: Ethics and Applications

The importance of explainability and responsible AI in the context of intelligent transportation is emphasized, particularly in AI-based control mechanisms such as traffic management systems and autonomous driving applications. The book discusses the challenges in the field and provides insights into prospective methods and techniques for enabling the interpretability of transportation systems, while also addressing ethical considerations.

Format: Hardback
Length: 90 pages
Publication date: 09 August 2022
Publisher: Springer International Publishing AG

Transportation is a critical realm that often involves life-or-death decisions, making it essential to ensure that these choices are not delegated to AI algorithms without proper explanation. The lack of transparency in such decision-making processes poses a significant threat to the safety and reliability of intelligent transportation systems. Therefore, the utmost importance of explainability and responsible AI cannot be overstated in this context.

In the realm of intelligent transportation systems (ITS), AI-based control mechanisms are becoming increasingly prevalent. These systems include traffic management systems and autonomous driving applications, where AI algorithms play a pivotal role in decision-making processes. However, the lack of explainability in these AI algorithms raises concerns about their accountability and reliability.

Explainable AI refers to the ability of AI systems to provide clear and comprehensible explanations for their decisions. It enables human users to understand the underlying logic and reasoning behind the algorithms' choices, thus promoting trust and confidence in the transportation system.

In the context of intelligent transportation, explainability is particularly crucial. It allows transportation professionals to assess the performance of AI algorithms, identify potential biases, and make informed decisions about their deployment. It also enables users to trust the system and feel comfortable relying on it for their daily commute.

Moreover, explainability is essential for addressing certain challenges in the field of autonomous vehicles. Autonomous vehicles rely heavily on AI algorithms to make split-second decisions, such as lane changes, speed adjustments, and collision avoidance. However, the lack of transparency in these algorithms can make it difficult to assess their reliability and trustworthiness.

Explainable AI can help address these challenges by providing insights into the decision-making process of autonomous vehicles. It can help identify potential biases and errors in the algorithms, enabling transportation professionals to rectify them and improve the overall performance of the system.

Similarly, explainability is crucial in traffic management systems. These systems use AI algorithms to optimize traffic flow, reduce congestion, and improve safety. However, the lack of transparency in these algorithms can make it difficult to assess their effectiveness and identify potential unintended consequences.

Explainable AI can help address these challenges by providing insights into the decision-making process of traffic management systems. It can help identify bottlenecks, congestion points, and areas of improvement, enabling transportation professionals to make data-driven decisions and optimize traffic flow.

Furthermore, explainability is essential for data integration and analytics in intelligent transportation systems. These systems rely on large amounts of data to make informed decisions and optimize traffic flow. However, the lack of transparency in the data processing and analysis can make it difficult to trust the results and make informed decisions.

Explainable AI can help address these challenges by providing insights into the data processing and analysis process. It can help identify potential biases, errors, and inconsistencies in the data, enabling transportation professionals to rectify them and improve the accuracy and reliability of the system.

In addition to technical considerations, ethical considerations are also crucial in the context of intelligent transportation. AI algorithms can make decisions that have significant implications for human safety and well-being. Therefore, it is essential to ensure that these algorithms are designed and implemented in a way that prioritizes ethical considerations, such as fairness, transparency, and accountability.

Explainable AI can help address ethical considerations by providing insights into the decision-making process of AI algorithms. It can help identify potential biases and errors in the algorithms, enabling transportation professionals to rectify them and ensure that the system is fair and unbiased.

To achieve explainability in intelligent transportation systems, several methods and techniques can be employed. These include using visualizations, natural language generation, and machine learning techniques to provide clear and comprehensible explanations for the AI algorithms' decisions.

Furthermore, it is important to involve human users in the development and evaluation of AI algorithms in intelligent transportation systems. This can help ensure that the algorithms are designed with human needs and preferences in mind and that they are transparent and accountable.

In conclusion, explainability and responsible AI are essential in the context of intelligent transportation. The lack of transparency in AI algorithms poses a significant threat to the safety and reliability of transportation systems. Therefore, it is crucial to prioritize the development and deployment of explainable AI systems that prioritize ethical considerations and provide clear and comprehensible explanations for their decisions. By doing so, we can build trust and confidence in intelligent transportation systems, enabling them to become a safer and more efficient mode of transportation for all.

Weight: 342g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031096433
Edition number: 1st ed. 2022