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Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability
Open accessAbstract
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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“The process often begins with the recognition of the need for ethical AI principles that sustain human values and rights [20], acknowledging the complex and opaque nature of AI systems and their potential to impact fairness, accountability, and transparency [21,22].”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [21]
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“First of all, the guidelines published by the Big Ten agree with the literature on the ethical development and deployment of AI: attempting to maximize the benefits of AI but also acknowledging the complex and opaque nature of AI systems and their potential limitations [21,22].”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [21]
In the citing paper
“In such a situation, the successes of bias mitigation efforts can be held as an essential metric toward measuring how well an organization aligns its applications of AI with ethical standards38–40.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [39]
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“This can result from biased data, faults in the design of the model used, or societal inequalities where inbuilt into training data sets.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [39]
In the citing paper
“Bias is a key ethical issue for AI - sometimes, the outcome of a computation can have an unacceptably discriminatory bias.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [39]
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“While some sectors have specific regulations, such as healthcare and finance, comprehensive and universally applicable AI regulations are still in nascent stages ( Dwivedi et al.,2021).”
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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)
- 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
- hybrid
- doi.org
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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