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                <text>Conference Papers</text>
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              <text>Explainable Artificial Intelligence: Frameworks for Ensuring the Trustworthiness</text>
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              <text>and Transparency; Autonomy; Control; Data Privacy; Fairness; Reliability; Safety and Security</text>
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              <text>The growing computer power and ubiquity of big data are allowing Artificial Intelligence (AI) to gain widespread adoption and applicability in a wide range of sectors. The absence of an explanation for the conclusions made by today's AI algorithms is a significant disadvantage in crucial decision-making systems. For example, existing black-box AI systems are vulnerable to bias and adversarial assaults, which can taint the learning and inference processes. Explainable AI (XAI) is a recent trend in AI algorithms that gives explanations for their AI conclusions. Many contemporary AI systems have been shown to be vulnerable to undetectable assaults, biased against underrepresented groups, and deficient in user privacy protection. These flaws damage the user experience and undermine people's faith in all AI systems. This study proposes a systematic way to tie the social science notions of trust to the technology employed in AI-based services and products.   2024 IEEE.</text>
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              <text>Shama U.; Sharma V.; Gupta V.</text>
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              <text>TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/TQCEBT59414.2024.10545063" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TQCEBT59414.2024.10545063&lt;/a&gt;
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              <text>ISBN: 979-835038427-7</text>
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              <text>Shama U., Christ University, Banglore, India; Sharma V., Christ University, Banglore, India; Gupta V., Christ University, Banglore, India</text>
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