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 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1×
“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).”
BackgroundBellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al. (2018) · arXiv · doi:10.48550/arxiv.1810.01943 · Cited by 1,581
Artificial intelligence in education: Addressing ethical challenges in K-12 settings
Akgun, S., & Greenhow, C. (2022) · AI and Ethics · doi:10.1007/s43681-021-00096-7 · Cited by 1,473
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
Explainable Deep Learning: A Field Guide for the Uninitiated
Ras, G., Xie, N., van Gerven, M. A. J., & Doran, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13200 · Cited by 551
Artificial Intelligence for a Better Future
Stahl, B. C. (2021) · SpringerBriefs in research and innovation governance · doi:10.1007/978-3-030-69978-9 · Cited by 437
Social Impact Assessment: Guidance for assessing and managing the social impacts of projects
Vanclay, F. M., Esteves, A. M., Aucamp, I., & Franks, D. M. (2015) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 361
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.07213 · Cited by 306 · cited 1×
“Open data sharing also allows for external scrutiny, enabling researchers, developers, and the public to assess the quality and fairness of datasets used for training AI models (Brundage et al., 2020).”
BackgroundPaulus, J. K., & Kent, D. M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0304-9 · Cited by 274
Demographic Bias in Biometrics: A Survey on an Emerging Challenge
Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., & Busch, C. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2992344 · Cited by 263
Research works printed but not linked: 5
- 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.
- 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.
- 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.
- 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.
- 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.
Cited by
Sorted by citations
A Comprehensive Survey of Deep Learning Approaches in Image Processing
Τρίγκα, Μ., & Dritsas, E. (2025) · Sensors · doi:10.3390/s25020531 · Cited by 174
AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
Wu, C., Zhang, H., & Carroll, J. M. (2024) · Future Internet · doi:10.3390/fi16100354 · Cited by 99
“…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 (Akinrinola et al. 2024; Diakopoulos 2020).”
Khan, M. U. A., Aslam, M. A., Abdullah, M. F., & Stojanović, G. M. (2024) · ACS Applied Bio Materials · doi:10.1021/acsabm.4c00362 · Cited by 78
Wang, W., Zhiguang, C., & Kuang, J. (2025) · Foods · doi:10.3390/foods14060976 · Cited by 95
AI meets physics: a comprehensive survey
Jiao, L., Song, X., You, C., Liu, X., Li, L., Chen, P., Tang, X., Feng, Z., Liu, F., Guo, Y., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10874-4 · Cited by 80
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
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
