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You can’t approach AI ethics with only formal procedures, say leaders at global retailer H&M Group. So to help build its collective moral compass, the company has built a culture of AI ethics based on experimentation. Its AI ethics training emphasizes concrete business examples and principles that teach people what to ask. H&M Group’s example shows that organizations can practice AI ethics and become better at it despite not knowing what “best” looks like.
Artificial intelligence changes how organizations work, and that’s one of the reasons why it challenges our ethics: Who should take responsibility for automated decisions and actions? How much agency should algorithms have? How should we organize interactions between minds and machines? How does technology affect the workforce? Where are biases built into the system?
Companies, regulators, and policy makers search for steadfast ethical principles to help them navigate these moral mazes. They follow what feels like a logical strategy: First, identify universal values (such as transparency, fairness, human autonomy, or explainability), then define applications of the values (such as decision-making or AI-supported recruitment processes), and, finally, formalize them in codes of conduct. The idea is that codes of conduct for AI should override the computational code of AI.
But perhaps this linear approach is too simplistic. German philosopher of technology Günther Anders warned of the “Promethean gap” that opens up between our power to imagine and invent new technologies and our ethical ability to understand and manage those technologies.1 With AI, the Promethean gap widens into a chasm. The rapid developments and disruptions of AI outpace the principled deliberations about rules to govern AI’s application.
Static, rule-based ethics cannot keep up with rapidly changing AI technologies because such technologies question the very base of our values and humanity itself, argue the authors of a report from Harvard’s Edmond & Lily Safra Center for Ethics.2 They suggest that an “ethics of experimentation,” rather than a rules-based approach, is the “only kind of framework that can gain traction on the realities of the moment.” For organizations using AI, this raises the question, how does an “AI ethics of experimentation” work in practice? And how can companies bridge the Promethean gap between moral imagination and AI’s technological power?
To get pragmatic, real-world answers, we teamed up with global fashion retailer H&M Group to research and learn from its approach to AI ethics. Linda Leopold, H&M Group’s head of AI strategy (which encompasses AI ethics) has spent the past six years embedding the responsible use of AI across the organization.
“Our strategy is built on a combination of governance and culture,” Leopold explained. “You can’t approach AI ethics only with formal procedures.
References
1. G. Anders, “Die Antiquiertheit des Menschen,” vol. 1 (Munich: C.H. Beck, 1956); and G. Anders, “Die Antiquiertheit des Menschen,” vol. 2 (Munich: C.H. Beck, 1980).
2. D. Allen, E. Frankel, W. Lim, et al., “Ethics of Decentralized Social Technologies: Lessons From Web3, the Fediverse, and Beyond,” PDF file (Cambridge, Massachusetts: Justice, Health & Democracy Impact Initiative, March 2023), https://ash.harvard.edu.
3. For a theoretical treatment of the idea, see M. Kornberger and S. Leixnering, “Moral Learning in Organizations: An Integrative Framework for Organizational Ethics,” Journal of Business Ethics, published online March 15, 2025.
4. See N. Epley and D. Tannenbaum, “Treating Ethics as a Design Problem,” Behavioral Science & Policy 3, no. 2 (2017): 72-84; and S.R. Martin, J.J. Kish-Gephart, and J.R. Detert, “Blind Forces: Ethical Infrastructures and Moral Disengagement in Organizations,” Organizational Psychology Review 4, no. 4 (November 2014): 295-325.
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