The Impact and Opportunities of Generative AI in Fact-Checking
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 24 of its 92 references that are in the map. First posted on arXiv 24 May 2024.
Builds on
24 of 92 references.
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12 more
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Built on by
Citing papers per year: 2024: 1; 2025: 8; 2026: 1.
10 papers.
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
- 2025A Survey of Scaling in Large Language Model Reasoningcited by 10
- 2024The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizationscited by 3
- 2025Not Wrong, But Untrue: LLM Overconfidence in Document-Based Queriescited by 3
- 2025Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknessescited by 1
- 2026Contradiction to Consensus: Dual Perspective, Multi Source Retrieval Based Claim Verification with Source Level Disagreement using LLMcited by 1
- 2025AIJIM: A Scalable Model for Real-Time AI in Environmental Journalismcited by 0
- 2025Fragments to Facts: Partial-Information Fragment Inference from LLMscited by 0
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Toolcited by 0
- 2025Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Modelscited by 0
- 2026Contradiction to Consensus: Dual Perspective, Multi Source Retrieval Based Claim Verification with Source Level Disagreement using LLM21 Feb 2026
- 2025Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models5 Nov 2025
- 2025Not Wrong, But Untrue: LLM Overconfidence in Document-Based Queries29 Sep 2025
- 2025Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses7 Aug 2025
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool9 Jul 2025
- 2025Fragments to Facts: Partial-Information Fragment Inference from LLMs20 May 2025
- 2025A Survey of Scaling in Large Language Model Reasoning2 Apr 2025
- 2025AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism19 Mar 2025
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking13 Feb 2025
- 2024The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizations4 Aug 2024