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 of establishing ethical AI guidelines often begins with recognition of the need for ethical AI principles that sustain human values and rights, acknowledging the complex and opaque nature of AI systems and their potential to impact fairness, accountability, and transparency.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe guidelines published by the Big Ten agree with the literature on the ethical development and deployment of AI, attempting to maximize AI's benefits while acknowledging the complex and opaque nature of AI systems and their potential limitations.
“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]
The success of bias mitigation efforts can be used as a metric for how well an organization aligns its AI applications with ethical standards.
“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]
AI outcomes can have discriminatory bias resulting from biased data, model design faults, or societal inequalities in training data.
“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]
Bias is a key ethical issue for AI.
“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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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× · see citing works
“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 · see citing works
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 · see citing works
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208 · see citing works
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 486 · see citing works
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 · see citing works
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× · see citing works
“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 · see citing works
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 · see citing works
Beyond the promise: implementing ethical AI
Eitel-Porter, R. (2021) · AI and Ethics · doi:10.1007/s43681-020-00011-6 · Cited by 245 · cited 1× · see citing works
“Their role is to develop, implement, and enforce ethical guidelines for AI development, ensuring alignment with organizational values and societal expectations (Eitel-Porter, 2021).”
BackgroundAI and Big Data: A blueprint for a human rights, social and ethical impact assessment
Mantelero, A. (2018) · Computer Law and Security Review · doi:10.1016/j.clsr.2018.05.017 · Cited by 203 · see citing works
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · arXiv · doi:10.48550/arxiv.2001.00973 · Cited by 179 · see citing works
Ethical issues in focus by the autonomous vehicles industry
Martinho, A. M., Herber, N., Kroesen, M., & Chorus, C. G. (2021) · Transport Reviews · doi:10.1080/01441647.2020.1862355 · Cited by 160 · see citing works
Smuha, N. A., Rengers, E., Harkens, A., Li, W., MacLaren, J., Piselli, R., & Yeung, K. (2021) · doi:10.2139/ssrn.3899991 · Cited by 153 · see citing works
Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities
Lim, H. S. M., & Taeihagh, A. (2019) · Sustainability · doi:10.3390/su11205791 · Cited by 136 · see citing works
Accountability and Transparency in Regulation: Critiques, Doctrines and Instruments
Lodge, M. C. (2004) · Edward Elgar Publishing eBooks · doi:10.4337/9781845420673.00015 · Cited by 128 · see citing works
Empowering political participation through artificial intelligence
Savaget, P., Chiarini, T., & Evans, S. (2018) · Science and Public Policy · doi:10.1093/scipol/scy064 · Cited by 99 · see citing works
Sarker, I. H. (2022) · Preprints.org · doi:10.20944/preprints202202.0001.v1 · Cited by 72 · see citing works
Stahl, B. C. (2021) · Repository@Nottingham (University of Nottingham) · doi:10.1007/978-3-030-69978-9 · Cited by 71 · see citing works
Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025) · AI and Ethics · doi:10.1007/s43681-023-00331-3 · Cited by 68 · see citing works
(2021) · SSRN Electronic Journal · Cited by 67 · see citing works
Allioui, H., & Mourdi, Y. (2023) · International Journal of Computer Engineering and Data Science · Cited by 65 · see citing works
Bias, awareness, and ignorance in deep-learning-based face recognition
Wehrli, S., Hertweck, C., Amirian, M., Glüge, S., & Stadelmann, T. (2021) · AI and Ethics · doi:10.1007/s43681-021-00108-6 · Cited by 43 · see citing works
Data protection by design: Building the foundations of trustworthy data sharing
Stalla-Bourdillon, S., Thuermer, G., Walker, J., Carmichael, L., & Simperl, E. P. B. (2020) · Data & Policy · doi:10.1017/dap.2020.1 · Cited by 39 · see citing works
Ethical Principles for Trustworthy AI
Nikolinakos, N. T. (2023) · Law, governance and technology series · doi:10.1007/978-3-031-27953-9_3 · Cited by 35 · see citing works
Considerations in Assuring Safety of Increasingly Autonomous Systems
Alves, E. E., Bhatt, D., Hall, B., Driscoll, K. R., Murugesan, A., & Rushby, J. (2018) · NASA Technical Reports Server (NASA) · Cited by 31 · see citing works
Thiruma Valavan, A. (2023) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2023.20.2.2245 · Cited by 21 · see citing works
Ethical Considerations in the Development and Deployment of AI Driven Software Systems
Konda, S. R. (2022) · International journal of computer science and technology · doi:10.5281/zenodo.10779119 · Cited by 13 · see citing works
Navigating Legal Frontiers: Exploring Emerging Issues in Cyber Law
Babikian, J. (. (2024) · doi:10.13140/rg.2.2.20264.55048 · Cited by 12 · see citing works
Empowering Political Participation through Artificial Intelligence
Savaget, P., & Chiarini, T. (2018) · Academy of Management Proceedings · doi:10.5465/ambpp.2018.14236abstract · Cited by 9 · see citing works
Data Protection by Design: Building the foundations of trustworthy data sharing
Stalla-Bourdillon, S., Thuermer, G., Walker, J., & Laura, C. (2019) · Research Portal (King's College London) · doi:10.5281/zenodo.3079895 · Cited by 9 · see citing works
Flagged first, then by citations
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.
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Τρίγκα, Μ., & Dritsas, E. (2025) · Sensors · doi:10.3390/s25020531 · Cited by 159 · see citing works
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 · see citing works
“…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 · see citing works
Wang, W., Zhiguang, C., & Kuang, J. (2025) · Foods · doi:10.3390/foods14060976 · Cited by 85 · see citing works
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 72 · see citing works
Olateju, O. O., Okon, S. U., Olaniyi, O. O., Samuel-Okon, A. D., & Asonze, C. U. (2024) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2024/v26i71206 · Cited by 52 · see citing works
Lin, M. P.-C., Liu, A. L., Poitras, E., Chang, M., & Chang, D. (2024) · Sustainability · doi:10.3390/su16208992 · Cited by 47 · see citing works
Influence of Leadership on Human–Artificial Intelligence Collaboration
Torres, R. A. Z., Rey-Sarmiento, C. F., Prado, J. C. A., Gómez-Cruz, N. A., Castro, D. Y. R., & Camargo, J. P. (2025) · Behavioral Sciences · doi:10.3390/bs15070873 · Cited by 46 · see citing works
Integrating AI Into Arbitration: Balancing Efficiency With Fairness and Legal Compliance
Alhasan, T. K. (2025) · Conflict Resolution Quarterly · doi:10.1002/crq.21470 · Cited by 42 · see citing works
Security paradigms for IoT in telecom networks: Conceptual challenges and solution pathways
Nzeako, G., Okeke, C. D., Akinsanya, M. O., Popoola, O. A., & Chukwurah, E. G. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i5.1111 · Cited by 40 · see citing works
Miller, T. I., Durlik, I., Kostecka, E., Łobodzińska, A., & Matuszak, M. (2024) · Energies · doi:10.3390/en17246271 · Cited by 44 · see citing works
Aldemir, C., & Uysal, T. U. (2025) · Administrative Sciences · doi:10.3390/admsci15020058 · Cited by 40 · see citing works
Navigating the Ethical Terrain of AI-Generated Text Tools: A Review
Mohamed, Y. A., Mohamed, A. H. H. M., Khanan, A., Bashir, M., Adiel, M. A. E., & Elsadig, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3521945 · Cited by 40 · see citing works
Akinsanya, M. O., Ekechi, C. C., & Okeke, C. D. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i4.1076 · Cited by 48 · see citing works
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Meduri, K., Podicheti, S., Satish, S., & Whig, P. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-4147-6.ch004 · Cited by 34 · see citing works
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Gunasekara, L., El-Haber, N., Nagpal, S., Moraliyage, H., Issadeen, Z., Manic, M., & Silva, D. D. (2025) · Applied System Innovation · doi:10.3390/asi8040097 · Cited by 56 · see citing works
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Onwubuariri, E. R., Adelakun, B. O., Olaiya, O. P., & Ziorklui, J. E. K. (2024) · Finance & Accounting Research Journal · doi:10.51594/farj.v6i6.1236 · Cited by 47 · see citing works
Hao, X., Demir, E., & Eyers, D. R. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101152 · Cited by 32 · see citing works
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Nadella, G. S., Meduri, S. S., Maturi, M. H., & Whig, P. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-4147-6.ch012 · Cited by 30 · see citing works
Aljohani, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-09569-9 · Cited by 30 · see citing works
AI and ethical accounting: Navigating challenges and opportunities
Adelakun, B. O., Majekodunmi, T. G., & Akintoye, O. S. (2024) · International Journal of Advanced Economics · doi:10.51594/ijae.v6i6.1230 · Cited by 38 · see citing works
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Simelane, P. M., & Kittur, J. (2024) · Computer Applications in Engineering Education · doi:10.1002/cae.22813 · Cited by 26 · see citing works
Ray, P. P. (2026) · Expert Systems · doi:10.1111/exsy.70213 · Cited by 31 · see citing works
SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS
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Quinn, T., & Thompson, O. K. (2024) · Research Square · doi:10.21203/rs.3.rs-4405206/v1 · Cited by 19 · see citing works
Empowering youth through sexuality and leadership education: Approaches and outcomes
Ediae, A. A., Chikwe, C. F., & Kuteesa, K. N. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.22.1.1214 · Cited by 18 · see citing works
THEORETICAL UNDERPINNINGS AND PRACTICAL IMPLICATIONS OF SD-WAN TECHNOLOGIES IN TELECOMMUNICATIONS
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Ediae, A. A., Chikwe, C. F., & Kuteesa, K. N. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.22.1.1213 · Cited by 16 · see citing works
Li-xin, Y., Alwabel, A. S. A., & Mohamad, U. H. (2025) · European Journal of Education · doi:10.1111/ejed.70351 · Cited by 26 · see citing works
Fang, C., Zhou, P., Zhang, X., He, Y., & Yang, Q. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1609827 · Cited by 14 · see citing works
Azfaralariff, A., Murad, M., Utra, U., Yussof, N. S., Fazial, F. F., Lazim, A. M., Fazry, S., & Law, D. S. N. (2025) · Food Reviews International · doi:10.1080/87559129.2025.2585134 · Cited by 14 · see citing works
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Kango, U. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_90 · Cited by 11 · see citing works
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Fasogbon, S. K., Fetuga, I. A., Oyeniran, A. T., Shaibu, S. A., Afolabi, S. O., Ndokwu, T. A., Oluwadare, S. R., Onafowokan, J. T., Eso, O. S., & Bassey, V. B. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115980 · Cited by 9 · see citing works
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Donkor, A., Kumi, D., Amponsah, E., & Atuwo-Ampoh, V. D. (2025) · Radiography · doi:10.1016/j.radi.2025.102953 · Cited by 8 · see citing works
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Lin, C.-Y., Brailovskaia, J., Üztemur, S., Gökalp, A., Değirmenci, N., Huang, P.-C., Chen, I.-H., Griffiths, M. D., & Pakpour, A. H. (2025) · Brain and Behavior · doi:10.1002/brb3.70648 · Cited by 8 · see citing works
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Bo, D., Ma’rof, A. A., & Zaremohzzabieh, Z. (2024) · Studies in Media and Communication · doi:10.11114/smc.v13i1.7212 · Cited by 7 · see citing works
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MoghadasNian, S. (2026) · doi:10.24108/preprints-3114361 · Cited by 7 · see citing works
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Praveenkumar, S., Anute, N., Vaissnave, V., Ragupathi, T., Fardale, S. K., Selvakumar, P., & Manjunath, T. C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2302-2.ch009 · Cited by 6 · see citing works
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Cheng, L., Chen, W., Li, R., & Zhang, C. (2025) · Sustainability · doi:10.3390/su17209196 · Cited by 6 · see citing works
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Azeroual, O., Schöpfel, J., Störl, U., & Marušić, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.058 · Cited by 6 · see citing works
Xiao, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2482739 · Cited by 6 · see citing works
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Kaur, J. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-3948-0.ch014 · Cited by 5 · see citing works
Start‐Up Employees Adoption of AI Technology for Innovation Ecosystems: An In‐Depth Exploration
Rizomyliotis, I., Κonstantoulaki, K., Battisti, E., & Do, B. (2025) · R and D Management · doi:10.1111/radm.12769 · Cited by 5 · see citing works
Lin, Z., Dou, H., & Lin, S. (2025) · Technovation · doi:10.1016/j.technovation.2025.103315 · Cited by 9 · see citing works
Dilemmas in AI Ethics: A Digital Game for Moral Reasoning and Collective Decision-Making
Sung-Eun, K. I. M., Kim, K., Lee, J., Ko, Y., Wang, Y., & So, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98417-4_31 · Cited by 5 · see citing works
Ethics in FinTech: a bibliometric analysis of research trends and key contributions
Martins, J. M., Asif, M., Dantas, R. M., Shahzad, M. F., & Shahbaz, M. (2025) · Journal of Financial Services Marketing · doi:10.1057/s41264-025-00315-w · Cited by 5 · see citing works
Ayanwale, M. A., Omeh, C. B., Oyeniran, F. M., & Kanu, C. C. (2025) · PLoS ONE · doi:10.1371/journal.pone.0335747 · Cited by 9 · see citing works
Navigating the Ethical Frontier-Human Oversight in AI-Driven Decision-Making System
Tariq, M. U. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6230-3.ch013 · Cited by 5 · see citing works
Ndiaye, S. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-75674-0_3 · Cited by 5 · see citing works
Zhong, W., Zhao, L., & Dou, Q. (2025) · International Journal of Information System Modeling and Design · doi:10.4018/ijismd.367278 · Cited by 4 · see citing works
AI as an Autonomous Decision-Maker in Governance: A New Era of Policy Formulation
Anand, S., & Mohamed, W. M. W. (2025) · Springer eBooks · doi:10.1007/978-3-031-89424-4_6 · Cited by 4 · see citing works
Fantus, S., Li, J., Wang, T., & Tang, L. (2026) · Journal of Medical Internet Research · doi:10.2196/79613 · Cited by 4 · see citing works
Responsible and Ethical AI in Natural Language Processing
Sangwan, S. R., & Kumar, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-88988-2_10 · Cited by 4 · see citing works
AI Assistants for Tour and Travel Operation
Talukder, M. B., & Muhsina, K. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-3498-0.ch013 · Cited by 4 · see citing works
Navigating Usability and User Experience in a Multi-Platform World With Agile Methodology
Sahu, S. K., & Ranganathan, S. (2024) · Advances in social networking and online communities book series · doi:10.4018/979-8-3693-2337-3.ch003 · Cited by 4 · see citing works
LEVERAGING GENERATIVE AI TO ENHANCE ENGINEERING EDUCATION AT BOTH LOW-LEVEL AND HIGH-LEVEL STUDY
Zhang, Z., & Chang, Y. (2024) · ASEE Middle Atlantic Section Spring Conference · doi:10.18260/1-2--49450 · Cited by 6 · see citing works
Key Drivers of Artificial Intelligence on Learner Engagement and Experience
Iyer, S. S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7220-3.ch004 · Cited by 3 · see citing works
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Gromova, E. A., & Ferreira, D. B. (2025) · Conflict Resolution Quarterly · doi:10.1002/crq.21482 · Cited by 3 · see citing works
Ethical Considerations in Using Technology for Medical Education
Adamopoulos, I., Thapa, P. P., & Syrou, N. F. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1519-5.ch009 · Cited by 4 · see citing works
FLIP: Tabletop Role-Play Game for AI Ethics Education through Cognitive Dissonance
Wang, Y., Ko, Y., Kim, K., Kim, S., & So, H. (2025) · Proceedings. · doi:10.22318/icls2025.945127 · Cited by 3 · see citing works
Atef, R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7584-7.ch007 · Cited by 5 · see citing works
AI-Driven Content Curation in Adtech: Enhancing User Engagement Through Personalized Advertising
Sharma, R. R., Dixit, D., Koduri, N. K., Kamineni, S., Kasimala, K., & Nair, R. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323967 · Cited by 3 · see citing works
Nazar, M., Unar, S., Ahmed, A., Su’ud, M. M., Alam, M., & Rahmat, A. (2026) · IEEE Access · doi:10.1109/access.2026.3664500 · Cited by 3 · see citing works
Zheng, H., Ou, M., Luo, C., & Zeng, Y. (2026) · Aslib Journal of Information Management · doi:10.1108/ajim-06-2025-0377 · Cited by 4 · see citing works
Samuel-Okon, A. D. (2024) · Asian Journal of Advanced Research and Reports · doi:10.9734/ajarr/2024/v18i9736 · Cited by 3 · see citing works
Steering the AI Revolution in Local Governments
Lahdili, N., & Nyadera, İ. N. (2025) · Advances in public policy and administration (APPA) book series · doi:10.4018/979-8-3693-8372-8.ch002 · Cited by 3 · see citing works
Olorunfemi, O., & Ogwu, P. C. (2025) · Digital Medicine · doi:10.1097/dm-2024-00016 · Cited by 3 · see citing works
Applying Ethics in AI-Based Research
Hashem, T. N., Ahmad, A., Kumar, A., & Alotoum, F. J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0340-6.ch012 · Cited by 3 · see citing works
A First Look at AI Trends in Value-Aligned Software Engineering Publications: Human-LLM Insights
Mougouei, D., Azarnik, A., Fahmideh, M., Mougouei, E., Dam, H. K., Khan, A. A., Rafi, S., Khan, J. A., & Ahmad, A. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS) · doi:10.1109/icse-seis66351.2025.00014 · Cited by 3 · see citing works
Shannaq, B. (2025) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2025.10.014 · Cited by 3 · see citing works
Exploring and Steering the Moral Compass of Large Language Models
Tlaie, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88223-4_30 · Cited by 8 · see citing works
El-Shihy, D. A. M., & Ismail, E. (2025) · المجلة العربیة للإدارة · doi:10.21608/aja.2025.352984.1779 · Cited by 3 · see citing works
Innovative Regulation of Open Source Intelligence and Deepfakes AI in Managing Public TrustPreprint
Obioha-Val, O. A., Gbadebo, M. O., Olaniyi, O. O., Chinye, N. C., & Balogun, A. Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5117977 · Cited by 3 · see citing works
Raj, A. A., Yang, M. H., & Yu, L. (2025) · Journal of Island and Marine Studies · doi:10.59711/jims.12.110015 · Cited by 2 · see citing works
TRANSFORMATIVE APPLICATIONS OF AI IN HEALTHCARE AND PHARMACEUTICAL INNOVATION
JASMINE, P., IBRAHIM, A., Siddiq, S., DHANURSHRA, A., NIRMALA, M., & BASHA, S. S. (2025) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2025v17i3.52719 · Cited by 2 · see citing works
Technology, Artificial Intelligence, and the Era of Information in Healthcare and Allied Sciences
Choudhury, S. R., Singh, K., Koner, S., & Ghosh, J. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9755-8.ch016 · Cited by 2 · see citing works
Ethical Challenges in Explainable AI: A Review on Cultural and Social Bias
Thalpage, N. S., Epa, D., & Jayawardhana, S. (2025) · Journal of Digital Art & Humanities · doi:10.33847/2712-8148.6.1_3 · Cited by 2 · see citing works
Fadare, S. A., Beterbo, J.-R. L., Ybanez, F., Isahac, J. R. A., Fadare, M. C., Barahimin, E. A., & Pandapatan, S. A. T. (2025) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt20252251 · Cited by 2 · see citing works
Measuring AI responsibility: A cross-country validation of a multidimensional framework
Jaturat, N., Na-Nan, K., & Hu, B. (2026) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2026.100388 · Cited by 2 · see citing works
Ethical Leadership and Governance Frameworks for Responsible AI Adoption in Higher Education
Atef, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1840-8.ch001 · Cited by 2 · see citing works
Fareed, M. M., & Shityakov, S. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100378 · Cited by 2 · see citing works
- 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
- Social Sciences › Safety Research › 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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