International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Explainable Ai in Autonomous Systems: Understanding the Reasoning Behind Decisions for Safety and Trust

Author(s) Ruchik Kashyapkumar Thaker
Country Canada
Abstract Autonomous systems, particularly autonomous vehicles (AVs), have advanced significantly over the past two decades, offering promising benefits for transportation in terms of safety, efficiency, and environmental sustainability. However, the opaque nature of AI-driven decision-making in these systems poses challenges for trust, societal acceptance, and regulatory compliance. To mitigate these concerns, the integration of Explainable AI (XAI) is critical. XAI provides transparent, interpretable reasoning behind autonomous decisions, ensuring accountability and alignment with ethical and legal standards. This paper explores the role of XAI in enhancing trust and transparency in AVs, reviewing current methodologies, proposing a framework for AV decision-making explainability, and discussing the regulatory implications. Additionally, the paper identifies the needs of key stakeholders, including developers, users, and regulators, and outlines future research directions to further improve the interpretability of AI-guided autonomous systems, fostering broader societal acceptance and trust.
Keywords Explainable AI (XAI), Autonomous Systems, Autonomous Vehicles (AVs), Robotics, Safety, regulatory compliance
Field Computer > Automation / Robotics
Published In Volume 4, Issue 6, November-December 2022
Published On 2022-12-07
Cite This Explainable Ai in Autonomous Systems: Understanding the Reasoning Behind Decisions for Safety and Trust - Ruchik Kashyapkumar Thaker - IJFMR Volume 4, Issue 6, November-December 2022. DOI 10.36948/ijfmr.2022.v04i06.29704
DOI https://doi.org/10.36948/ijfmr.2022.v04i06.29704
Short DOI https://doi.org/g8pnh8
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