Understanding of Ethical Artificial Intelligence and Implementation using SAP AI

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© 2024 by IJCTT Journal
Volume-72 Issue-6
Year of Publication : 2024
Authors : Sandeep Kumar
DOI :  10.14445/22312803/IJCTT-V72I6P113

How to Cite?

Sandeep Kumar, "Understanding of Ethical Artificial Intelligence and Implementation using SAP AI," International Journal of Computer Trends and Technology, vol. 72, no. 6, pp. 94-100, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I6P113

Abstract
As Artificial Intelligence (AI) continues to permeate various aspects of daily life, the imperative for ethical considerations escalates. This abstract explores the multifaceted realm of AI ethics, spotlighting critical issues and contemporary frameworks that govern the responsible development and deployment of AI technologies. The core ethical challenges addressed include bias, which can manifest through skewed data sets, leading to discriminatory outcomes; autonomy, emphasizing the need for human oversight in automated decisions; and transparency, advocating for comprehensible AI systems to ensure accountability. These issues underscore the tension between the advancement of AI capabilities and the upholding of ethical standards. The discussion extends to the ethical frameworks shaping the landscape of AI ethics, from professional codes of conduct to regulatory guidelines like the GDPR and principles-based frameworks that uphold values such as beneficence and justice. The narrative also acknowledges the role of algorithmic accountability and participatory design, advocating for the involvement of diverse stakeholders in AI development to foresee and mitigate ethical risks. This synthesis not only articulates the importance of ethics in AI but also calls for an equilibrium between innovation and ethical responsibility, propelling the conversation towards the establishment of AI that is equitable, transparent, and aligned with human values.

Keywords
Business Intelligence (BI), Data Intelligence, SAP Cloud, SAP HANA, Data mining, Artificial intelligence, SAP BW4HANA, SAP BTP, SAP Cloud, SAP Analytics Cloud (SAC).

Reference

[1] Ramnath Balasubramanian, Ari Libarikian, and Doug McElhaney, “Insurance 2030—The Impact of AI on the Future of Insurance,” McKinsey & Company, pp. 1-10, 2018.
[Google Scholar]
[2] Trevor Maynard et al., “What Role for AI in Insurance Pricing,” A Preprint, 2019.
[Google Scholar]
[3] Malgorzata Śmietanka, Adriano Koshiyama, and Philip Treleaven, “Algorithms in Future Insurance Markets,” International Journal of Data Science and Big Data Analytics, vol. 1, no. 1, pp. 1-19, 2021.
[Google Scholar] [Publisher Link]
[4] Chris Lamberton, Damiano Brigo, and Dave Hoy, “Impact of Robotics, RPA and AI on the Insurance Industry: Challenges and Opportunities,” Journal of Financial Perspectives, vol. 4, no. 1, 2017.
[Google Scholar] [Publisher Link]
[5] Michael Foucault, The Foucault Effect: Studies in Governmentality, University of Chicago Press, pp. 1-307, 1991.
[Google Scholar] [Publisher Link]
[6] K.H. Borch, A. Sandmo, and K.K. Aase, Economics of Insurance, Elsevier Science, pp. 1-410, 2014.
[Google Scholar] [Publisher Link]
[7] Alex Zarifis, Christopher P. Holland, and Alistair Milne, “Evaluating the Impact of AI on Insurance: The Four Emerging AI-and Datadriven Business Models,” Emerald Open Research, vol. 1, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Anat Lior, “Insuring AI: The Role of Insurance in Artificial Intelligence Regulation,” Harvard Journal of Law & Technology, vol. 35, no. 2, 2021.
[Google Scholar] [Publisher Link]
[9] Naman Kumar, Jayant Dev Srivastava, and Harshit Bisht, “Artificial Intelligence in Insurance Sector,” Journal of the Gujarat Research Society, vol. 21, no. 7, pp. 79-91, 2019.
[Google Scholar]