Operationalizing AI Ethics: A Thematic Synthesis for Adaptive and Layered Governance
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411-428
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10.58578/masaliq.v6i1.8880
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Authors:Irhas Irhas1*
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Abstract
This study is motivated by the growing discourse on artificial intelligence (AI) ethics that has not yet been fully operationalized in practice, particularly in the health and education sectors in the era of generative AI. It aims to synthesize the literature on the operationalization of AI ethics in order to propose a new governance model that is more adaptive and context-sensitive. A structured thematic synthesis was conducted on peer-reviewed studies published between 2020 and 2025. The analysis reveals a central paradox: a strong global consensus on the principles of justice, transparency, accountability, and privacy stands in stark contrast to significant gaps in their implementation. The emerging challenges are highly context-dependent: in the health sector, they center on technical safety and relational accountability, whereas in the education sector they are more closely related to epistemic integrity and equitable access. The advent of generative AI exacerbates these issues by introducing new systemic risks, such as large-scale hallucinations and an increasingly profound crisis of clarity. In response to the limitations of static compliance-based approaches, this study proposes the Adaptive and Layered AI Governance Model (ALAIGoM), a model of adaptive and layered AI governance that integrates operationalized ethical principles, multi-level governance structures, and a continuous ethics life cycle to enable context-sensitive, adaptive oversight. The model is grounded in proactive trust-building, human-centered design, and mechanisms that ensure acceptability. Overall, this study offers a coherent roadmap for reconfiguring AI ethics from a mere normative checklist into an embedded, adaptive, and responsive governance process.
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References
Abràmoff, M. D., Roehrenbeck, C., Trujillo, S., Goldstein, J., Graves, A. S., Repka, M. X., & Silva, E. Z., III. (2022). A reimbursement framework for artificial intelligence in healthcare. npj Digital Medicine, 5(1), Article 72. https://doi.org/10.1038/s41746-022-00621-w
Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V. I., & Precise4Q Consortium. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310. https://doi.org/10.1186/s12911-020-01332-6
Aquino, Y. S. J., Rogers, W. A., Braunack-Mayer, A., Frazer, H., Win, K. T., Houssami, N., Degeling, C., Semsarian, C., & Carter, S. M. (2023). Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills. International Journal of Medical Informatics, 169, 104903. https://doi.org/10.1016/j.ijmedinf.2022.104903
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. International Journal of Information Management, 62, 102433. https://doi.org/10.1016/j.ijinfomgt.2021.102433
Bingley, W. J., Curtis, C., Lockey, S., Bialkowski, A., Gillespie, N., Haslam, S. A., Ko, R. K. L., Steffens, N., Wiles, J., & Worthy, P. (2023). Where is the human in human-centered AI? Insights from developer priorities and user experiences. Computers in Human Behavior, 141, 107617. https://doi.org/10.1016/j.chb.2022.107617
Bleher, H., & Braun, M. (2023). Reflections on putting AI ethics into practice: How three AI ethics approaches conceptualize theory and practice. Science and Engineering Ethics, 29(3), 21. https://doi.org/10.1007/s11948-023-00443-3
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1), 1–12. https://doi.org/10.1057/s41599-023-02079-x
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Choung, H., David, P., & Ross, A. (2023). Trust and ethics in AI. AI & Society, 38(2), 733–745. https://doi.org/10.1007/s00146-022-01473-4
Cobianchi, L., Verde, J. M., Loftus, T. J., Piccolo, D., Dal Mas, F., Mascagni, P., Garcia Vazquez, A., Ansaloni, L., Marseglia, G. R., Massaro, M., Gallix, B., Padoy, N., Peter, A., & Kaafarani, H. M. (2022). Artificial intelligence and surgery: Ethical dilemmas and open issues. Journal of the American College of Surgeons, 235(2), 268–275. https://doi.org/10.1097/XCS.0000000000000242
Creswell, J. W., & Poth, C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: A framework for governance. Ethics and Information Technology, 23(3), 505–525. https://doi.org/10.1007/s10676-021-09593-z
Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 53, 103662. https://doi.org/10.1016/j.frl.2023.103662
Elendu, C., Amaechi, D. C., Elendu, T. C., Jingwa, K. A., Okoye, O. K., John Okah, M., Ladele, J. A., Farah, A. H., & Alimi, H. A. (2023). Ethical implications of AI and robotics in healthcare: A review. Medicine, 102(50), e36671. https://doi.org/10.1097/MD.0000000000036671
Floridi, L., & Cowls, J. (2021). A unified framework of five principles for AI in society. In Philosophical Studies Series (pp. 5–17). Springer International Publishing. https://doi.org/10.1007/978-3-030-81907-1_2
Fukuda-Parr, S., & Gibbons, E. (2021). Emerging consensus on “ethical AI”: Human rights critique of stakeholder guidelines. Global Policy, 12(S6), 32–44. https://doi.org/10.1111/1758-5899.12965
Gevaert, C. M. (2022). Explainable AI for earth observation: A review including societal and regulatory perspectives. International Journal of Applied Earth Observation and Geoinformation, 112, 102869. https://doi.org/10.1016/j.jag.2022.102869
Harrer, S. (2023). Attention is not all you need: The complicated case of ethically using large language models in healthcare and medicine. EBioMedicine, 90, 104512. https://doi.org/10.1016/j.ebiom.2023.104512
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
Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50. https://doi.org/10.1016/j.bushor.2019.09.003
Kong, S.-C., Cheung, M.-Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence, 6, 100214. https://doi.org/10.1016/j.caeai.2024.100214
Kusters, R., Misevic, D., Berry, H., Cully, A., Le Cunff, Y., Dandoy, L., Díaz-Rodríguez, N., Ficher, M., Grizou, J., Othmani, A., Palpanas, T., Komorowski, M., Loiseau, P., Moulin-Frier, C., Nanini, S., Quercia, D., Sebag, M., Soulié-Fogelman, F., Taleb, S., ... Wehbi, F. (2020). Interdisciplinary research in artificial intelligence: Challenges and opportunities. Frontiers in Big Data, 3, 577974. https://doi.org/10.3389/fdata.2020.577974
Lameras, P., & Arnab, S. (2021). Power to the teachers: An exploratory review on artificial intelligence in education. Information, 13(1), 14. https://doi.org/10.3390/info13010014
Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 36–49. https://doi.org/10.1037/amp0000972
Larson, D. B., Magnus, D. C., Lungren, M. P., Shah, N. H., & Langlotz, C. P. (2020). Ethics of using and sharing clinical imaging data for artificial intelligence: A proposed framework. Radiology, 295(3), 675–682. https://doi.org/10.1148/radiol.2020192536
Li, Y., Wu, B., Huang, Y., & Luan, S. (2024). Developing trustworthy artificial intelligence: Insights from research on interpersonal, human-automation, and human-AI trust. Frontiers in Psychology, 15, 1382693. https://doi.org/10.3389/fpsyg.2024.1382693
Lyons, H., Velloso, E., & Miller, T. (2021). Conceptualising contestability. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–25. https://doi.org/10.1145/3449180
Marcus, H. J., Ramirez, P. T., Khan, D. Z., Layard Horsfall, H., Hanrahan, J. G., Williams, S. C., Beard, D. J., Bhat, R., Catchpole, K., Cook, A., Hutchison, K., Martin, J., Melvin, T., Stoyanov, D., Rovers, M., Raison, N., Dasgupta, P., Noonan, D., Stocken, D., ... IDEAL Robotics Colloquium. (2024). The IDEAL framework for surgical robotics: Development, comparative evaluation and long-term monitoring. Nature Medicine, 30(1), 61–75. https://doi.org/10.1038/s41591-023-02732-7
McLean, S., Read, G. J. M., Thompson, J., Baber, C., Stanton, N. A., & Salmon, P. M. (2023). The risks associated with artificial general intelligence: A systematic review. Journal of Experimental & Theoretical Artificial Intelligence, 35(5), 649–663. https://doi.org/10.1080/0952813X.2021.1964003
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297
Morley, J., Elhalal, A., Garcia, F., Kinsey, L., Mökander, J., & Floridi, L. (2021). Ethics as a Service: A pragmatic operationalisation of AI ethics. Minds and Machines, 31(2), 239–256. https://doi.org/10.1007/s11023-021-09563-w
Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). Sage Publications.
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC methods programme. ESRC Methods Programme.
Sison, A. J. G., Daza, M. T., Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2024). ChatGPT: More than a “weapon of mass deception” ethical challenges and responses from the human-centered artificial intelligence (HCAI) perspective. International Journal of Human–Computer Interaction, 40(17), 4853–4872. https://doi.org/10.1080/10447318.2023.2225931
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Stahl, B. C., Rodrigues, R., Santiago, N., & Macnish, K. (2022). A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values. Computer Law & Security Review, 45, 105661. https://doi.org/10.1016/j.clsr.2022.105661
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 20539517221115189. https://doi.org/10.1177/20539517221115189
Tóth, Z., Caruana, R., Gruber, T., & Loebbecke, C. (2022). The dawn of the AI robots: Towards a new framework of AI robot accountability. Journal of Business Ethics, 178(4), 895–916. https://doi.org/10.1007/s10551-022-05050-z
Vetter, M. A., Lucia, B., Jiang, J., & Othman, M. (2024). Towards a framework for local interrogation of AI ethics: A case study on text generators, academic integrity, and composing with ChatGPT. Computers and Composition, 71, 102831. https://doi.org/10.1016/j.compcom.2024.102831
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7–30. https://doi.org/10.15678/EBER.2023.110201
Zembylas, M. (2023). A decolonial approach to AI in higher education teaching and learning: Strategies for undoing the ethics of digital neocolonialism. Learning, Media and Technology, 48(1), 25–37. https://doi.org/10.1080/17439884.2021.2010094