Authors:
Muniraju Hullurappa

Addresses:
Department of Data Governance, System Soft Technologies, Texas, United States of America.  muhullurappa@santanderconsumerusa.com

Abstract:

Computing and artificial intelligence (AI) are advancing at a pace that offers opportunities to benefit human life, from healthcare and education to transportation and entertainment. However, they also pose a variety of ethical dilemmas that society will need to solve to ensure that their use is responsible and just. This study provides an inclusive examination of the ethics of computing and AI, making a comprehensive consideration based on the history of past practices currently prevailing and prospects. By integrating quantitative data with qualitative observations over a mixed-method approach, the study captures much of the complexity and depth of the ethical field. Many results raise concerns about ethics, particularly in the areas of privacy, autonomy, and bias. The authors offer a variety of practical recommendations to developers, policy-makers, and users on the basis of these reflections to help encourage the practice of ethics in AI development and deployment. Focusing on transparency, accountability, and inclusion in AI systems, these recommendations argue for the development of strong ethical standards and oversight mechanisms for the increasingly complex ethical landscape of AI and computing technologies. This study addresses these challenges, aiming to help change the way AI is experienced across society by developing innovations while enhancing efficiency and delivering them fairly and justly to create a world in which an unequal distribution of AI opportunities does not exist.

Keywords: Ethics and Computing; Artificial Intelligence; Bias and Privacy; Review of Case Studies; Subject Matter Expert Interviews; Safety Quotient (SQ); Ethical Score (ES).

Received on: 02/12/2023, Revised on: 17/02/2024, Accepted on: 05/04/2024, Published on: 03/06/2024

DOI: 10.69888/FTSCS.2024.000199

FMDB Transactions on Sustainable Computing Systems, 2024 Vol. 2 No. 2, Pages: 74-83

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