Ethical-technological governance of generative artificial intelligence: toward a human-centered integrity framework in contexts of digital innovation
DOI:
https://doi.org/10.64439/cisai.v2i2.46Keywords:
Generative artificial intelligence, algorithmic governance, technological jurisprudence, digital integrity, human centered ethics, algorithmic transparencyAbstract
The expansion of generative artificial intelligence has revealed that the abundance of ethical principles contrasts with the scarcity of verifiable mechanisms to apply them. In response to this gap, the study designs and evaluates an ethical-technological integrity framework that steers digital innovation toward algorithmic transparency and the safeguarding
of fundamental rights without inhibiting technical development. Following the design science paradigm, the framework was built from five theory-derived requirements and a document analysis of Regulation (EU) 2024/1689, the NIST framework, and China’s provisions on generative AI, coded through a matrix of eight indicators. The results show that none of the three regimes fully guarantees binding human veto or the participation of affected groups. In response, the framework proposes four axes, namely auditability, proactive accountability, participatory bias mitigation, and a risk-segmented escalation protocol. Its demonstration in a social protection public service indicates that the framework is operable and turns ethics into a strategic asset by generating auditable evidence.
References
[1] J. Sevilla, L. Heim, A. Ho, T. Besiroglu, M. Hobbhahn, and P. Villalobos, “Compute trends across three eras of machine learning,” in 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1–8. doi: 10.1109/IJCNN55064.2022.9891914.
[2] D. Ganguli, D. Hernandez, L. Lovitt, A. Askell, Y. Bai, A. Chen et al., “Predictability and surprise in large generative models,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 1747–1764. doi: 10.1145/3531146.3533229.
[3] Y. K. Dwivedi, N. Kshetri, L. Hughes, E. L. Slade, A. Jeyaraj, A. K. Kar et al., ““So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” International Journal of Information Management, vol. 71, p. 102642, 2023. doi: 10.1016/j.ijinfomgt.2023.102642.
[4] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023. doi: 10.1145/3571730.
[5] E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp. 610–623. doi: 10.1145/3442188.3445922.
[6] L. Weidinger, J. Uesato, M. Rauh, C. Griffin, P.-S. Huang, J. Mellor et al., “Taxonomy of risks posed by language models,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 214–229. doi: 10.1145/3531146.3533088.
[7] N. A. Smuha, “From a ‘race to AI’ to a ‘race to AI regulation’: Regulatory competition for artificial intelligence,” Law, Innovation and Technology, vol. 13, no. 1, pp. 57–84, 2021. doi: 10.1080/17579961.2021.1898300.
[8] M. Veale, K. Matus, and R. Gorwa, “AI and global governance: Modalities, rationales, tensions,” Annual Review of Law and Social Science, vol. 19, pp. 255–275, 2023. doi: 10.1146/annurev-lawsocsci-020223-040749.
[9] M. Veale and F. Zuiderveen Borgesius, “Demystifying the Draft EU Artificial Intelligence Act: Analysing the good, the bad, and the unclear elements of the proposed approach,” Computer Law Review International, vol. 22, no. 4, pp. 97–112, 2021. doi: 10.9785/cri-2021-220402.
[10] J. Laux, S. Wachter, and B. Mittelstadt, “Trustworthy artificial intelligence and the European Union AI Act: On the conflation of trustworthiness and acceptability of risk,” Regulation & Governance, vol. 18, no. 1, pp. 3–32, 2024. doi: 10.1111/rego.12512.
[11] A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, vol. 1, no. 9, pp. 389–399, 2019. doi: 10.1038/s42256-019-0088-2.
[12] B. Mittelstadt, “Principles alone cannot guarantee ethical AI,” Nature Machine Intelligence, vol. 1, no. 11, pp. 501–507, 2019. doi: 10.1038/s42256-019-0114-4.
[13] T. Hagendorff, “The ethics of AI ethics: An evaluation of guidelines,” Minds and Machines, vol. 30, no. 1, pp. 99–120, 2020. doi: 10.1007/s11023-020-09517-8.
[14] J. Morley, L. Floridi, L. Kinsey, and A. Elhalal, “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, vol. 26, no. 4, pp. 2141–2168, 2020. doi: 10.1007/s11948-019-00165-5.
[15] L. Floridi and J. Cowls, “A unified framework of five principles for AI in society,” Harvard Data Science Review, vol. 1, no. 1, 2019. doi: 10.1162/99608f92.8cd550d1.
[16] A. R. Hevner, S. T. March, J. Park, and S. Ram, “Design science in information systems research,” MIS Quarterly, vol. 28, no. 1, pp. 75–105, 2004. doi: 10.2307/25148625.
[17] P. Hacker, A. Engel, and M. Mauer, “Regulating ChatGPT and other large generative AI models,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023, pp. 1112–1123. doi: 10.1145/3593013.3594067.
[18] J. Mökander, J. Schuett, H. R. Kirk, and L. Floridi, “Auditing large language models: A three-layered approach,” AI and Ethics, vol. 4, no. 4, pp. 1085–1115, 2024. doi: 10.1007/s43681-023-00289-2.
[19] C. Novelli, M. Taddeo, and L. Floridi, “Accountability in artificial intelligence: What it is and how it works,” AI & Society, vol. 39, no. 4, pp. 1871–1882, 2024. doi: 10.1007/s00146-023-01635-y.
[20] E. Papagiannidis, P. Mikalef, and K. Conboy, “Responsible artificial intelligence governance: A review and research framework,” The Journal of Strategic Information Systems, vol. 34, no. 2, p. 101885, 2025. doi: 10.1016/j.jsis.2024.101885.
[21] A. Matthias, “The responsibility gap: Ascribing responsibility for the actions of learning automata,” Ethics and Information Technology, vol. 6, no. 3, pp. 175–183, 2004. doi: 10.1007/s10676-004-3422-1.
[22] F. Santoni de Sio and G. Mecacci, “Four responsibility gaps with artificial intelligence: Why they matter and how to address them,” Philosophy & Technology, vol. 34, no. 4, pp. 1057–1084, 2021. doi: 10.1007/s13347-021-00450-x.
[23] S. Wachter, B. Mittelstadt, and L. Floridi, “Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation,” International Data Privacy Law, vol. 7, no. 2, pp. 76–99, 2017. doi: 10.1093/idpl/ipx005.
[24] M. E. Kaminski, “The right to explanation, explained,” Berkeley Technology Law Journal, vol. 34, no. 1, pp. 189–218, 2019. doi: 10.15779/Z38TD9N83H.
[25] J. J. Bryson, “Patiency is not a virtue: The design of intelligent systems and systems of ethics,” Ethics and Information Technology, vol. 20, no. 1, pp. 15–26, 2018. doi: 10.1007/s10676-018-9448-6.
[26] C. Cath, S. Wachter, B. Mittelstadt, M. Taddeo, and L. Floridi, “Artificial intelligence and the ‘good society’: The US, EU, and UK approach,” Science and Engineering Ethics, vol. 24, no. 2, pp. 505–528, 2018. doi: 10.1007/s11948-017-9901-7.
[27] L. Floridi, J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, C. Luetge, R. Madelin, U. Pagallo, F. Rossi, B. Schafer, P. Valcke, and E. Vayena, “AI4People—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations,” Minds and Machines, vol. 28, no. 4, pp. 689–707, 2018. doi: 10.1007/s11023-018-9482-5.
[28] D. Schiff, J. Borenstein, J. Biddle, and K. Laas, “AI ethics in the public, private, and NGO sectors: A review of a global document collection,” IEEE Transactions on Technology and Society, vol. 2, no. 1, pp. 31–42, 2021. doi: 10.1109/TTS.2021.3052127.
[29] L. Floridi, “Translating principles into practices of digital ethics: Five risks of being unethical,” Philosophy & Technology, vol. 32, no. 2, pp. 185–193, 2019. doi: 10.1007/s13347-019-00354-x.
[30] A. Kasirzadeh and I. Gabriel, “In conversation with artificial intelligence: Aligning language models with human values,” Philosophy & Technology, vol. 36, no. 2, p. 27, 2023. doi: 10.1007/s13347-023-00606-x.
[31] S. Zuboff, “Big other: Surveillance capitalism and the prospects of an information civilization,” Journal of Information Technology, vol. 30, no. 1, pp. 75–89, 2015. doi: 10.1057/jit.2015.5.
[32] A. Birhane, “Algorithmic injustice: A relational ethics approach,” Patterns, vol. 2, no. 2, p. 100205, 2021. doi: 10.1016/j.patter.2021.100205.
[33] T. Birkstedt, M. Minkkinen, A. Tandon, and M. Mäntymäki, “AI governance: Themes, knowledge gaps and future agendas,” Internet Research, vol. 33, no. 7, pp. 133–167, 2023. doi: 10.1108/INTR-01-2022-0042.
[34] M. Whittaker, “The steep cost of capture,” Interactions, vol. 28, no. 6, pp. 50–55, 2021. doi: 10.1145/3488666.
[35] M. Taddeo, T. McCutcheon, and L. Floridi, “Trusting artificial intelligence in cybersecurity is a double-edged sword,” Nature Machine Intelligence, vol. 1, no. 12, pp. 557–560, 2019. doi: 10.1038/s42256-019-0109-1.
[36] S. Gregor and A. R. Hevner, “Positioning and presenting design science research for maximum impact,” MIS Quarterly, vol. 37, no. 2, pp. 337–355, 2013. doi: 10.25300/MISQ/2013/37.2.01.
[37] K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A design science research methodology for information systems research,” Journal of Management Information Systems, vol. 24, no. 3, pp. 45–77, 2007. doi: 10.2753/MIS0742-1222240302.
[38] M. Ananny and K. Crawford, “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability,” New Media & Society, vol. 20, no. 3, pp. 973–989, 2018. doi: 10.1177/1461444816676645.
[39] S. L. Blodgett, S. Barocas, H. Daumé III, and H. Wallach, “Language (technology) is power: A critical survey of “bias” in NLP,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 5454–5476. doi: 10.18653/v1/2020.acl-main.485.
[40] A. Birhane, W. Isaac, V. Prabhakaran, M. Díaz, M. C. Elish, I. Gabriel, and S. Mohamed, “Power to the people? Opportunities and challenges for participatory AI,” in Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’22), 2022, pp. 1–8. doi: 10.1145/3551624.3555290.
[41] B. Green, “The flaws of policies requiring human oversight of government algorithms,” Computer Law & Security Review, vol. 45, p. 105681, 2022. doi: 10.1016/j.clsr.2022.105681.
[42] R. Parasuraman and D. H. Manzey, “Complacency and bias in human use of automation: An attentional integration,” Human Factors, vol. 52, no. 3, pp. 381–410, 2010. doi: 10.1177/0018720810376055.
[43] I. Solaiman, “The gradient of generative AI release: Methods and considerations,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023, pp. 111–122. doi: 10.1145/3593013.3593981.
[44] G. A. Bowen, “Document analysis as a qualitative research method,” Qualitative Research Journal, vol. 9, no. 2, pp. 27–40, 2009. doi: 10.3316/QRJ0902027.
[45] Parlamento Europeo y Consejo de la Unión Europea, “Reglamento (UE) 2024/1689 del Parlamento Europeo y del Consejo, de 13 de junio de 2024, por el que se establecen normas armonizadas en materia de inteligencia artificial (Reglamento de Inteligencia Artificial),” Diario Oficial de la Unión Europea, Serie L, 12 de julio de 2024. Disponible en: http://data.europa.eu/eli/reg/2024/1689/oj
[46] National Institute of Standards and Technology, “Artificial intelligence risk management framework (AI RMF 1.0),” National Institute of Standards and Technology, Gaithersburg, MD, Tech. Rep. NIST AI 100-1, 2023. doi: 10.6028/NIST.AI.100-1.
[47] National Institute of Standards and Technology, “Artificial intelligence risk management framework: Generative artificial intelligence profile,” National Institute of Standards and Technology, Gaithersburg, MD, Tech. Rep. NIST AI 600-1, 2024. doi: 10.6028/NIST.AI.600-1.
[48] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, 2006. doi: 10.1191/1478088706qp063oa.
[49] J. Venable, J. Pries-Heje, and R. Baskerville, “FEDS: A framework for evaluation in design science research,” European Journal of Information Systems, vol. 25, no. 1, pp. 77–89, 2016. doi: 10.1057/ejis.2014.36.
[50] H. Roberts, J. Cowls, J. Morley, M. Taddeo, V. Wang, and L. Floridi, “The Chinese approach to artificial intelligence: An analysis of policy, ethics, and regulation,” AI & Society, vol. 36, no. 1, pp. 59–77, 2021. doi: 10.1007/s00146-020-00992-2.
[51] M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, and T. Gebru, “Model cards for model reporting,” in Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019, pp. 220–229. doi: 10.1145/3287560.3287596.
[52] T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumé III, and K. Crawford, “Datasheets for datasets,” Communications of the ACM, vol. 64, no. 12, pp. 86–92, 2021. doi: 10.1145/3458723.
[53] A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, 2020. doi: 10.1016/j.inffus.2019.12.012.
[54] I. D. Raji, A. Smart, R. N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. Smith-Loud, D. Theron, and P. Barnes, “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, 2020, pp. 33–44. doi: 10.1145/3351095.3372873.
[55] I. D. Raji, P. Xu, C. Honigsberg, and D. Ho, “Outsider oversight: Designing a third party audit ecosystem for AI governance,” in Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, pp. 557–571. doi: 10.1145/3514094.3534181.
[56] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,” ACM Computing Surveys, vol. 54, no. 6, pp. 1–35, 2021. doi: 10.1145/3457607.
[57] A. Birhane and V. U. Prabhu, “Large image datasets: A pyrrhic win for computer vision?” in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 1536–1546. doi: 10.1109/WACV48630.2021.00158.
[58] E. Perez, S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving, “Red teaming language models with language models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 3419–3448. doi: 10.18653/v1/2022.emnlp-main.225.
[59] J. Metcalf, E. Moss, E. A. Watkins, R. Singh, and M. C. Elish, “Algorithmic impact assessments and accountability: The co-construction of impacts,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp. 735–746. doi: 10.1145/3442188.3445935.
[60] K. Goddard, A. Roudsari, and J. C. Wyatt, “Automation bias: A systematic review of frequency, effect mediators, and mitigators,” Journal of the American Medical Informatics Association, vol. 19, no. 1, pp. 121–127, 2012. doi: 10.1136/amiajnl-2011-000089.
[61] B. Shneiderman, “Human-centered artificial intelligence: Reliable, safe & trustworthy,” International Journal of Human–Computer Interaction, vol. 36, no. 6, pp. 495–504, 2020. doi: 10.1080/10447318.2020.1741118.
[62] E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for modern deep learning research,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 13693–13696. doi: 10.1609/aaai.v34i09.7123.
[63] J. Cowls, A. Tsamados, M. Taddeo, and L. Floridi, “The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations,” AI & Society, vol. 38, no. 1, pp. 283–307, 2023. doi: 10.1007/s00146-021-01294-x.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Computational Innovations, Intelligent Systems and AI
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
