The Impact and Opportunities of Generative AI in Fact-Checking

TL;DR

The paper investigates how generative AI could transform fact-checking and the information ecosystem. It employs a qualitative interview study with 38 participants across 29 fact-checking organizations to map opportunities and challenges, and to illuminate design implications. A novel sociotechnical Verification dimension is proposed to guide content production and verification, contextualized within the TOE framework and IFCN principles to articulate value tensions. The authors outline nine research directions focused on fairness, accountability, and transparency, emphasizing collaboration with fact-checkers and the need to protect human labor and editorial integrity. The work provides a principled, human-centered framework to inform both scholarly understanding and practical adoption of AI in high-stakes information verification.

Abstract

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.

Figures (4)

• Figure 1: Incorporating a society-altering technology like generative AI into sociotechnical fact-checking work requires "intangible" investments brynjolfsson2021productivity (new processes and skills) to realize its potential without deprioritizing the values of fact-checking or displacing the role of human experts.
• Figure 2: A description of In Use, In Progress, and Envisioned generative technologies grouped according to five fact-checking infrastructures.
• Figure 3: A description of the Technological, Organizational, and Environmental challenges to generative AI in fact-checking
• Figure 4: Designing for verification: A sociotechnical verification space for the production and verification of content.