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Cited by 206Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Digitalization, Law, and Regulation

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability

Open access
Olatunji Akinrinola, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile, Chinonye Esther Ugochukwu·2024·GSC Advanced Research and Reviews
DOI 10.30574/gscarr.2024.18.3.0088
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Abstract

As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.

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Research works printed but not linked: 5

  1. Ben, K. (2023). The Influence of Cultural Diversity on AI Ethics: A Global Comparative Analysis. International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 25-30.
  2. Cihon, P. (2019). Standards for AI governance: international standards to enable global coordination in AI research & development. Future of Humanity Institute. University of Oxford, 340-342.
  3. Goldstein, B. E., & Butler, W. H. (2010). Expanding the scope and impact of collaborative planning: combining multi-stakeholder collaboration and communities of practice in a learning network. Journal of the American Planning Association, 76(2), 238-249.
  4. Lysaght, T., Lim, H. Y., Xafis, V., & Ngiam, K. Y. (2019). AI-assisted decision-making in healthcare: the application of an ethics framework for big data in health and research. Asian Bioethics Review, 11, 299-314.
  5. Nassar, A., & Kamal, M. (2021). Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations. International Journal of Responsible Artificial Intelligence, 11(8), 1-11.

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Metadata

Authors
Akinrinola, O., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E.
Corresponding author
Olatunji Akinrinola
Venue
GSC Advanced Research and Reviews
Publisher
GSC Online Press
Published
2024-03-30
Volume
18
Issue
3
Pages
050-058
ISSN
2582-4597 (ISSN-L)
DOI
10.30574/gscarr.2024.18.3.0088
OpenAlex
W4392621839
Cited by
206
References
30
Citations by year
2026: 63 · 2025: 100 · 2024: 43
FWCI
85.09
Type
journal article
Language
en
Open access
Licence
cc-by-nc-sa
Locations
GSC Advanced Research and Reviews (publishedVersion, cc-by-nc-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
AI ethics, algorithmic bias, algorithmic fairness, explainable AI, transparency, accountability, fairness metrics, AI governance, human-in-the-loop, open data sharing, AI ethics framework, AI regulation, responsible AI

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