International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Volume 6 Issue 5
September-October 2024
Indexing Partners
Demystifying Explainable AI: Understanding, Transparency, and Trust
Author(s) | Muthukrishnan Muthusubramanian, Suhas Jangoan, Kapil Kumar Sharma, Gowrisankar Krishnamoorthy |
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Country | United States |
Abstract | Artificial intelligence (AI) has emerged as a transformative technology with vast potential to revolutionize industries and societies. However, the responsible development, deployment, and governance of AI technologies require addressing complex ethical, regulatory, and societal challenges. This research paper aims to demystify Explainable AI (XAI) and explore its implications for understanding, transparency, and trust in AI systems. Through a comprehensive review of the literature, we examine key concepts, methodologies, and applications of XAI, as well as ethical considerations, regulatory frameworks, international cooperation, and societal impacts of AI. The paper highlights the importance of transparency, fairness, and accountability in AI governance and emphasizes the need for interdisciplinary collaboration and stakeholder engagement to ensure the responsible and ethical development of AI technologies. By fostering a deeper understanding of XAI and its implications, this paper contributes to the ongoing dialogue on the ethical and responsible use of AI in society. |
Keywords | Artificial Intelligence, Explainable AI, XAI, Transparency, Trust, Ethical Considerations, Regulatory Frameworks, International Cooperation, Societal Impact, Responsible AI Governance |
Field | Engineering |
Published In | Volume 6, Issue 2, March-April 2024 |
Published On | 2024-03-04 |
Cite This | Demystifying Explainable AI: Understanding, Transparency, and Trust - Muthukrishnan Muthusubramanian, Suhas Jangoan, Kapil Kumar Sharma, Gowrisankar Krishnamoorthy - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14597 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i02.14597 |
Short DOI | https://doi.org/gtmbgr |
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