Guardrails versus Gatekeepers: Understanding Product Managers' Ethical Decision-Making in Generative AI

What is the role of product managers (PMs) in the responsible use of generative AI (genAI) in products and everyday work— and what enables or constrains their ability to take action? Past literature has examined the ways in which organizational policies can become decoupled from practices when incentives for responsible action are misaligned or impeded by profit motives. While the role of engineers and professional ethicists in the context of AI has been examined in detail, the role of product managers—who are frequently portrayed as “gatekeepers” or critical decision-makers in product teams—remains unclear, particularly regarding genAI. In this paper, we examine what organizational conditions promote responsible use of genAI by product managers by drawing on twenty-five interviews and a global survey of over three hundred respondents in product management-related roles. First, we find that uncertainty around responsible AI and a sense of diffused responsibility constrain ethical action, while leadership commitment and organizational principles enable ethical action—making some responsible practices up to fourteen times more likely. Further, our study finds two sets of actions product managers take to “recouple” ethical commitments and practices. The first set includes low-resource, individual actions product managers can implement without explicit organizational incentives (e.g., individual or team-wide reviews and safeguarding standards around data privacy). The second set includes high-resource, collective actions that require organizational incentives (e.g., conducting audits or delaying shipment of products). In contrast to existing findings in the literature, product managers view themselves as "guardrails" rather than “gatekeepers” when it comes to making ethics-related decisions for genAI use. Our research suggests that recoupling ethical policies and practices at the level of product teams requires institutional buy-in and higher level leadership commitment. Nevertheless, we show that individual actors are able to exhibit agency through some meaningful, low resource actions, even in the absence of organizational incentive structures even in the absence of organizational incentives, though this alone is insufficient to operationalize responsible AI at scale. CCS Concepts: • Computing methodologies → Artificial intelligence; • Social and professional topics → Socio- technical systems. ∗These authors share lead authorship. Authors’ Contact Information: Genevieve Smith, genevieve.smith@berkeley.edu, University of California, Berkeley, Berkeley, CA, USA; Natalia Luka, nataliyan@berkeley.edu, University of California, Berkeley, Berkeley, CA, USA; Merrick Osborne, Cornell University, Ithaca, NY, USA; Brian Lattimore, Stanford University, Palo Alto, CA, USA; Jessica Newman, University of California, Berkeley, Berkeley, CA, USA; Brent Mittelstadt, University of Oxford, Oxford, UK; Brandie Nonnecke, University of California, Berkeley, Berkeley, CA, USA. This work is licensed under a Creative Commons Attribution 4.0 International License. FAccT ’26, Montreal, QC, Canada © 2026 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-2596-8/2026/06 https://doi.org/10.1145/3805689.3812395

Authors: Genevieve Smith, Natalia Luka, Merrick Osborne, Brian Lattimore, Jessica Newman, Brent Mittelstadt, Brandie Nonnecke

Published in: Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

Publication date: 2026-06-25

Read the paper: https://doi.org/10.1145/3805689.3812395

Source license: Creative Commons Attribution 4.0 International — https://creativecommons.org/licenses/by/4.0/

The authors and publisher do not sponsor or endorse this recording.