Generative AI appears poised to transform white collar professions, with more than 90% of Fortune 500 companies using OpenAI's flagship GPT models, which have been characterized as "general purpose technologies" capable of effecting epochal changes in the economy. But how will such technologies impact organizations whose job is to verify and report factual information, and to ensure the health of the information ecosystem? To investigate this question, we conducted 30 interviews with N=38 participants working at 29 fact-checking organizations across six continents, asking about how they use generative AI and the opportunities and challenges they see in the technology. We found that uses of generative AI envisioned by fact-checkers differ based on organizational infrastructure, with applications for quality assurance in Editing, for trend analysis in Investigation, and for information literacy in Advocacy. We used the TOE framework to describe participant concerns ranging from the Technological (lack of transparency), to the Organizational (resource constraints), to the Environmental (uncertain and evolving policy). Building on the insights of our participants, we describe value tensions between fact-checking and generative AI, and propose a novel Verification dimension to the design space of generative models for information verification work. Finally, we outline an agenda for fairness, accountability, and transparency research to support the responsible use of generative AI in fact-checking. Throughout, we highlight the importance of human infrastructure and labor in producing verified information in collaboration with AI. We expect that this work will inform not only the scientific literature on fact-checking, but also contribute to understanding of organizational adaptation to a powerful but unreliable new technology.

翻译：生成式人工智能似乎即将改变白领职业，超过90%的财富500强公司正在使用OpenAI的旗舰GPT模型，这些模型被描述为能够引发经济时代性变革的"通用目的技术"。然而，此类技术将如何影响那些以核实和报道事实信息、维护信息生态系统健康为使命的机构？为探究此问题，我们对来自六大洲29个事实核查机构的N=38名从业者进行了30次访谈，调研他们使用生成式AI的现状以及对该技术机遇与挑战的认知。研究发现，事实核查工作者设想的生成式AI应用因组织基础设施而异：编辑环节侧重于质量保证，调查环节聚焦趋势分析，倡导环节关注信息素养。我们运用TOE框架系统梳理了参与者的关切，涵盖技术层面（缺乏透明度）、组织层面（资源限制）与环境层面（不确定且持续演变的政策）。基于参与者的深刻见解，我们揭示了事实核查与生成式AI之间的价值张力，并为信息验证工作的生成模型设计空间提出了创新的验证维度。最后，我们规划了促进生成式AI在事实核查中负责任使用的公平性、问责制与透明度研究议程。全文始终强调人类基础设施与劳动在协同AI生产验证信息过程中的核心作用。本研究不仅可为事实核查领域的学术文献提供参考，更有助于理解组织如何适应这项强大却不可靠的新兴技术。