Building Trustworthy AI Systems: Developing Explainable Models for Transparent Decision-Making in Autonomous Vehicles
DOI:
https://doi.org/10.36676/j.sust.sol.v1.i4.21Keywords:
Explainable Models, Transparent Decision-Making, Autonomous VehiclesAbstract
The emergence of autonomous vehicles (AVs) represents a critical turning point in the development of transportation, with the potential to completely transform how we move while improving accessibility, efficiency, and safety. However, faith in these systems' decision-making processes becomes critical as they advance in sophistication and become more interwoven into daily life. For AVs to be widely accepted and deployed safely, reliable AI systems—especially those that are transparent and explainable—must be developed. This paper investigates the idea of creating reliable artificial intelligence (AI) systems, with a particular emphasis on creating explicable models for transparent decision-making in autonomous cars.
References
• Atakishiyev, S., Salameh, M. and Goebel, R., 2024. Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles. arXiv preprint arXiv:2404.07383. DOI: https://doi.org/10.1109/IV55156.2024.10588812
• Chamola, V., Hassija, V., Sulthana, A.R., Ghosh, D., Dhingra, D. and Sikdar, B., 2023. A review of trustworthy and explainable artificial intelligence (xai). IEEe Access. DOI: https://doi.org/10.1109/ACCESS.2023.3294569
• Madhav, A.S. and Tyagi, A.K., 2022, July. Explainable Artificial Intelligence (XAI): connecting artificial decision-making and human trust in autonomous vehicles. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021 (pp. 123-136). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-19-1142-2_10
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Sustainable Solutions
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The license allows sharing and adapting the material as long as it is not for commercial purposes, and proper attribution is given to the authors.