Navigating ethical challenges of explainable ai in autonomous systems

Joseph Chukwunweike 1, *, Oluwarotimi Ayodele Lawal 2, John Babatope Arogundade 3 and Bolape Alad e 4

1 Automation and Process Control Engineer, Gist Limited, United Kingdom.
2 Department of Mechanical Engineering, Wolverhampton, United Kingdom.
3 School of Management, University of Bradford, United Kingdom.
4 Department of Mechanical Engineering, Federal University of Technology Akure, Nigeria.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 1807–1819.
Article DOI: 10.30574/ijsra.2024.13.1.1872
Publication history: 
Received on 18 August 2024; revised on 30 September 2024; accepted on 02 October 2024
 
Abstract: 
The rapid integration of autonomous systems, such as vehicles, drones, and robots, into various sectors brings forth significant ethical challenges concerning their decision-making processes. This paper examines the role of Explainable AI (XAI) in addressing these challenges, particularly regarding accountability in the event of accidents and the necessity of human oversight in automated environments. We discuss the critical ethical implications of transparency, emphasizing how XAI can bridge the gap between complex algorithmic decision-making and public understanding, thereby fostering trust in these technologies. The paper also outlines the current regulatory frameworks for AI safety, analysing their effectiveness in promoting responsible innovation. Furthermore, we investigate the consequences of opaque algorithms, especially in life-critical applications where the stakes are exceptionally high. Through an analysis of case studies, we showcase how organizations have successfully implemented XAI to enhance safety measures and uphold ethical responsibility in their autonomous systems. Ultimately, this study advocates for the integration of XAI as a vital component in developing responsible autonomous technologies, ensuring accountability, and safeguarding public trust in an era increasingly defined by automation.
 
Keywords: 
Explainable AI; Autonomous systems; Ethical challenges; Accountability; AI safety regulations; Public trust
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0