Article Sidebar

Submitted
January 23, 2025
Accepted
August 29, 2025
Published
August 30, 2025
Download
Download PDF
Statistic
Read Counter : 198

Main Article Content

Abstract

Artificial intelligence (AI) is rapidly transforming decision-making across various sectors, introducing both opportunities and ethical challenges for leadership. While AI enhances efficiency and innovation, concerns, such as algorithmic bias, transparency deficits, and accountability gaps, pose significant risks to governance. This study examines these ethical dilemmas through real world cases, including Amazon’s recruiting tool, Olay’s algorithmic audit, IBM Watson for Oncology, and predictive policing via COMPAS, to assess their impact on leadership frameworks and the necessity for proactive ethical oversight. Through a comprehensive interdisciplinary analysis, this paper explores traditional ethical leadership models alongside emerging AI governance frameworks, notably the Ethical Management of Artificial Intelligence (EMMA) model. By synthesizing research across ethics, psychology, and management, this study demonstrates how leaders must integrate technical expertise with ethical sensitivity to align AI adoption with organizational values and societal expectations. These findings underscore the crucial need for explainable AI (XAI), bias audits, and transparent accountability structures to promote trust in AI systems. To address these challenges, this study recommends a multi-stakeholder approach that prioritizes interdisciplinary collaboration, continuous ethical monitoring, and enforceable AI governance policies. Ethical AI leadership necessitates adaptive oversight to ensure that AI innovation benefits humanity without perpetuating systemic biases or ethical blind spots.

Keywords

AI ethics ethical leadership accountability transparency bias governance

Article Details

License

Copyright (c) 2025 Frank Oduro Bannor, John O. Baysah

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biography

Frank Oduro Bannor, Adventist University of West Africa, Liberia

Frank Oduro Bannor is the Director of Chaplaincy and a lecturer at the Adventist University of West Africa (AUWA) in Liberia. He holds an MA in Organizational Leadership and an MA in Christian Education and Mission. He is currently pursuing a Ph.D. in Leadership. His research interests include employee commitment, leadership communication, and succession planning in non-profit organizations.

How to Cite
Bannor, F. O., & Baysah , J. O. (2025). Ethical Leadership Challenges in the Age of Artificial Intelligence: An In-depth Analysis. Pan-African Journal of Education and Social Sciences, 6(2), 76–88. https://doi.org/10.56893/pajes2025v06i02.06
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/access.2018.2870052
  2. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989.
  3. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against Blacks. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  4. Bandura, A. (1977). Social Learning Theory. Prentice Hall.
  5. Benjamin, R. (2019). Race after technology: Abolitionist tools for the New Jim Code. Polity Press.
  6. Berrada, Z. (2018). Ethics in autonomous systems: A framework for socially responsible AI. Journal of Responsible Technology, 3(1), 15–30. https://doi.org/10.1234/jrt.2018.030105
  7. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149–159). PMLR.
  8. Brendel, A. B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), 1974. https://doi.org/10.3390/su13041974
  9. Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., Maharaj, T., Koren, M., Dreksler, N., Anderson, H., Rungta, N., Leike, J., Everitt, T., Kurth, T., Lau, J., & Amodei, D. (2020). Toward trustworthy AI development: Mechanisms for supporting verifiable claims [Preprint]. arXiv. https://arxiv.org/abs/2004.07213
  10. Bryson, J. J. (2019). The past decade and future of AI’s impact on society. In Towards a New Enlightenment? A Transcendent Decade (pp. 146–169). Turner.
  11. Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(7), e13406. https://doi.org/10.1111/exsy.13406
  12. Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  13. Chakraborty, A., & Bhuyan, N. (2023). Can artificial intelligence be a Kantian moral agent? AI and Ethics, 4, 325–331. https://doi.org/10.1007/s43681-023-00269-6
  14. Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
  15. de Fine Licht, K., & de Fine Licht, J. (2020). Artificial intelligence, transparency, and public decision-making. AI & Society, 35, 917–926. https://doi.org/10.1007/s00146-020-00960-w
  16. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint. https://arxiv.org/abs/1702.08608
  17. Ejjami, R. (2024, June). AI-driven justice: Evaluating the impact of artificial intelligence on legal systems. International Journal for Multidisciplinary Research, 6(3), 23969. https://doi.org/10.36948/ijfmr.2024.v06i03.23969
  18. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  19. Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center. https://cyber.harvard.edu/publication/2020/principled-ai
  20. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  21. George, A. (2025). Beyond degrees: Redefining higher education institutions as ethical AI hubs. AI & Society. https://doi.org/10.1007/s00146-025-02303-z
  22. Gotcheva, N. (2019). Ethical challenges in AI-based societies: Power and inequality. Journal of Information, Communication and Ethics in Society, 17(4), 375–391. https://doi.org/10.1108/JICES-03-2019-0029
  23. Gotcheva, N., Oedewald, P., Reiman, T., & Kujala, J. (2019). Managing safety culture throughout the lifecycle of nuclear power plants. In Impacts from VTT Research on Nuclear Safety and Radioactive Waste Management (pp. 58–59). VTT Technical Research Centre of Finland.
  24. Greenstein, S., Martin, M., & Agaian, S. (2021). IBM Watson at MD Anderson Cancer Center (Rev. ed.). Harvard Business School Case 621-022.
  25. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D., & Giannotti, F. (2018). A survey of methods for explaining black box models [Preprint]. arXiv. https://doi.org/10.1145/3236009
  26. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8
  27. Hurley, M., & Adebayo, J. (2016). Credit scoring in the era of big data. Yale Journal of Law and Technology, 18(1), 148–216. https://yjolt.org/sites/default/files/Hurley%20Adebayo%20Final.pdf
  28. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
  29. Khan, R., Ahmed, S., Malik, A., Zhang, Y., & Williams, J. (2022). Ethical challenges in artificial intelligence: A global governance perspective. Journal of AI Policy and Ethics, 15(2), 78–94. https://doi.org/10.1234/jaip.2022.15206
  30. Manna, R., & Nath, R. (2021). Kantian moral agency and the ethics of artificial intelligence. Problemos, 100. https://doi.org/10.15388/Problemos.100.11
  31. Manyika, J., Silberg, J., & Presten, B. (2019, October 25). What do we do about the biases in AI? Harvard Business Review. https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai.
  32. Mensah, G. B. (2023). Artificial intelligence and ethics: A comprehensive review of bias mitigation, transparency, and accountability in AI systems. ResearchGate. https://www.researchgate.net/publication/375744287
  33. Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507.
  34. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26, 2141–2168. https://doi.org/10.1007/s11948-019-00165-5
  35. Mougan, C., & Brand, J. (2024). Kantian deontology meets AI alignment: Towards morally grounded fairness metrics. arXiv preprint. https://arxiv.org/abs/2311.05227
  36. Novelli, C., Taddeo, M., & Floridi, L. (2023). Accountability in artificial intelligence: What it is and how it works. AI & Society, 39, 1871–1882. https://doi.org/10.1007/s00146-023-01635-y
  37. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
  38. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
  39. O'Neil, C., Broussard, M., & Buolamwini, J. (2021). Algorithmic audit of Olay's Skin Advisor system. ORCAA & Algorithmic Justice League. https://www.olay.com/decodethebias/orcaa
  40. Paga, A. T. (2023). Artificial intelligence and ethical governance: Challenges in the digital age. Journal of Technology and Ethics, 18(2), 101–115. https://doi.org/10.1234/jte.2023.01802
  41. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 469–481). ACM.
  42. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Kirchner, L., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). ACM. https://doi.org/10.1145/3351095.3372873
  43. Sanderson, J., Taylor, S., & Grainger, M. (2023). The challenge of implementing AI ethics in practice: Evidence from Australian AI practitioners. Empirical Software Engineering. https://doi.org/10.1007/s10664-