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