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International Journal Of Engineering, Business And Management(IJEBM)

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  4. Principles Governing Ethical Development and Deployment of AI

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Principles Governing Ethical Development and Deployment of AI

Shubh Shukla


International Journal of Engineering, Business And Management(IJEBM), Vol-8,Issue-2, April - June 2024, Pages 26-46 , 10.22161/ijebm.8.2.5

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Article Info: Received: 01 May 2024; Received in revised form: 01 Jun 2024; Accepted: 07 Jun 2024; Available online: 15 Jun 2024

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The ethical development and deployment of artificial intelligence (AI) is a rapidly evolving field with significant implications for society. This paper delves into the multifaceted ethical considerations surrounding AI, emphasising the importance of transparency, accountability, and privacy. By conducting a comprehensive review of existing literature and case studies, it highlights key ethical issues such as bias in AI algorithms, privacy concerns, and the societal impact of AI technologies. The study underscores the necessity for robust governance frameworks and international collaboration to address these ethical challenges effectively. It explores the need for ongoing ethical evaluation as AI technologies advance, particularly in autonomous systems. The paper emphasises the importance of integrating ethical principles into AI design from the outset, fostering sustainable practices, and raising awareness through education. Furthermore, the paper examines current regulatory frameworks across various regions, comparing their effectiveness in promoting ethical AI practices. The findings suggest a global consensus on key ethical principles, though their implementation varies widely. By proposing strategies to ensure responsible AI innovation and mitigate risks, this research contributes to the ongoing discourse on the future of AI ethics, aiming to guide the development of AI technologies that uphold human dignity and contribute to the common good. Research the ethical considerations and societal impacts of AI, focusing on issues like bias in AI algorithms, privacy concerns, or the effect on employment. This can involve a comprehensive review of existing literature and case studies.

AI ethics, Transparency, Accountability, Privacy, Bias in AI algorithms

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