Ethical AI and Data Integrity: Ensuring Responsible AI Innovation Through Quantum - Resistant Algorithms and Federated Learning Paradigms
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Informative Article | Data & Knowledge Engineering | India | Volume 12 Issue 1, January 2023 | Popularity: 5.4 / 10


     

Ethical AI and Data Integrity: Ensuring Responsible AI Innovation Through Quantum - Resistant Algorithms and Federated Learning Paradigms

Abhijit Joshi


Abstract: Artificial Intelligence (AI) and machine learning are transforming industries by automating processes, enhancing decision - making, and uncovering insights from vast datasets. However, these advancements come with significant ethical challenges, including issues of bias, fairness, transparency, and accountability. This paper explores these ethical challenges and proposes a comprehensive framework for developing and deploying ethical AI systems. By examining case studies and best practices from industry leaders, we provide actionable guidelines for ensuring data integrity and responsible AI innovation. Through detailed methodologies, pseudocode, and visual aids, we aim to equip data engineers with the tools necessary to create AI systems that are both effective and ethically sound.


Keywords: Ethical AI, Data Integrity, Responsible AI, Bias in AI, AI Transparency, AI Fairness, Generative AI, Federated Learning, Quantum - Resistant Algorithms, AI Accountability


Edition: Volume 12 Issue 1, January 2023


Pages: 1297 - 1304


DOI: https://www.doi.org/10.21275/SR24615150919



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Abhijit Joshi, "Ethical AI and Data Integrity: Ensuring Responsible AI Innovation Through Quantum - Resistant Algorithms and Federated Learning Paradigms", International Journal of Science and Research (IJSR), Volume 12 Issue 1, January 2023, pp. 1297-1304, https://www.ijsr.net/getabstract.php?paperid=SR24615150919, DOI: https://www.doi.org/10.21275/SR24615150919

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