Ethics Suppression in AI Development: A Comparative Case Study of Responsible AI Under Competitive Pressure

Authors

DOI:

https://doi.org/10.51137/wrp.ijarbm.869

Keywords:

Ethics Suppression, Responsible AI, AI Governance, Ethical Oversight, Competitive Pressure, AI Safety, Organisational Ethics, Governance Authority

Abstract

This study explores the governance tensions among Google, OpenAI, Meta, and Microsoft, focusing on how competitive AI development can lead to the ethics suppression. The study aims to examine how ethical oversight loses practical influence even when responsible AI structures are formally in place, especially during rapid technological competition. Despite the rapid growth of responsible AI frameworks and ethical principles in the tech sector, there has been less focus on how these concerns are actually applied when pressure to deploy increases. This paper introduces the concept of ethics suppression in AI development, highlighting that while ethical oversight may exist on paper, its practical authority can diminish during decision-making. Using a comparative qualitative case study examining corporate governance documents, public statements, and reports on governance issues, we identify common issues such as reduced authority, a weakened ability to escalate concerns, urgency in deployment, and fragmented governance. Our findings indicate that heightened competition can weaken the influence of ethical oversight, even in organisations with established responsible AI practices. By differentiating ethics suppression from ethics washing, it offers a framework for organisations and policymakers to evaluate the effectiveness of ethical oversight in competitive AI development

References

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

Bietti, E. (2020). From ethics washing to ethics bashing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 210–219. (FAccT ’20) https://doi.org/10.1145/3351095.3372860

Bird, F.G., & Waters, J.A. (1989). The moral muteness of managers. California Management Review, 32, 73-88.

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., et al. (2021). On the opportunities and risks of foundation models. Stanford Center for Research on Foundation Models. https://arxiv.org/abs/2108.07258

Christensen, C. (1997). The innovator’s dilemma. Cambridge, MA: Harvard Business School Press.

Corrêa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E. P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E., & De Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10), 100857. https://doi.org/10.1016/j.patter.2023.100857

D’Onfro, J. (2019, April 4). Google scraps its AI ethics board less than two weeks after launch in the wake of employee protest. Forbes. https://www.forbes.com/sites/jilliandonfro/2019/04/04/google-cancels-its-ai-ethics-board-less-than-two-weeks-after-launch-in-the-wake-of-employee-protest/

Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385

Gillespie, T. (2018). Custodians of the internet: platforms, content moderation, and the hidden decisions that shape social media. Yale University Press. https://doi.org/10.12987/9780300235029

Google. (2018). AI principles. Google. https://ai.google/principles/

Greene, D., Hoffmann, A. L., & Stark, L. (2019). Better, nicer, clearer, fairer: a critical assessment of the movement for ethical artificial intelligence and machine learning. Proceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2019.258

Hao, K. (2020, December 4). We read the paper that forced Timnit Gebru out of Google. Here’s what it says. MIT Technology Review. https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-research-paper-forced-out-timnit-gebru/

Harvard Law Review (2025, April 10). Amoral drift in AI corporate governance - Harvard Law Review. Harvard Law Review. https://harvardlawreview.org/print/vol-138/amoral-drift-in-ai-corporate-governance/

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

Levy, M. G. (2021). Timnit Gebru says artificial intelligence needs to slow down. Wired. Retrieved February 1, 2024, from https://www.wired.com/story/rewired-2021-timnit-gebru/

Meta. (2023). Responsible AI at Meta. Meta AI. https://ai.meta.com/responsible-ai/

Metcalf, J., Moss, E., & boyd, danah. (2019). Owning ethics: corporate logics, silicon valley, and the institutionalisation of ethics. Social Research: An International Quarterly, 86(2), 449–476. https://doi.org/10.1353/sor.2019.0022

Meyer, J. W., & Rowan, B. (1977). Institutionalised organisations: formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340–363. https://doi.org/10.1086/226550

Mickle, T., Metz, C., Isaac, M., & Weise, K. (2023, December 9). Inside OpenAI’s crisis over the future of artificial intelligence. The New York Times. https://www.nytimes.com/2023/12/09/technology/openai-altman-inside-crisis.html

Microsoft. (2024). Responsible AI principles and approach | Microsoft AI. Www.microsoft.com. https://www.microsoft.com/en-us/ai/principles-and-approach

Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4

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

NIST. (2023). National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://www.nist.gov/itl/ai-risk-management-framework

OECD. (2019). OECD principles on artificial intelligence. OECD Publishing. https://oecd.ai/en/ai-principles

OpenAI. (2023). OpenAI preparedness framework. OpenAI. https://openai.com/

Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2). https://doi.org/10.1016/j.jsis.2024.101885

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873

Ropek, L. (2026, February 11). OpenAI disbands mission alignment team | TechCrunch. TechCrunch. https://techcrunch.com/2026/02/11/openai-disbands-mission-alignment-team-which-focused-on-safe-and-trustworthy-ai-development/

Stake, R. E. (1995). The Art of Case Study Research. Sage Publications

Tan K. W. K. (2024, August 28). OpenAI has lost nearly half of its AGI safety team, says ex-researcher. Business Insider. https://www.businessinsider.com/openai-lost-nearly-half-agi-safety-team-ex-researcher-2024-8

Tenbrunsel, A. E., & Messick, D. M. (2004). Ethical fading: the role of self-deception in unethical behaviour. Social Justice Research, 17(2), 223–236. https://doi.org/10.1023/b:sore.0000027411.35832.53

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Thousand Oaks, CA: Sage.

Downloads

Published

2026-08-14

Issue

Section

Original Research Paper

How to Cite

Frimpong, V. (2026). Ethics Suppression in AI Development: A Comparative Case Study of Responsible AI Under Competitive Pressure. International Journal of Applied Research in Business and Management, 7(9). https://doi.org/10.51137/wrp.ijarbm.869

Most read articles by the same author(s)