# The Impact and Opportunities of Generative AI in Fact-Checking

arXiv: https://arxiv.org/abs/2405.15985 · HTML: https://mlsanity.com/paper/2405.15985/

## 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 (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Queer In AI: A Case Study in Community-Led Participatory AI](https://mlsanity.com/paper/2303.16972/) (2023)
- [Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers](https://mlsanity.com/paper/2309.12570/) (2023)
- [Scientists' Perspectives on the Potential for Generative AI in their Fields](https://mlsanity.com/paper/2304.01420/) (2023)
- [Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations](https://mlsanity.com/paper/2205.03931/) (2022)
- [Human and technological infrastructures of fact-checking](https://mlsanity.com/paper/2205.10894/) (2022)
- [The Design Space of Generative Models](https://mlsanity.com/paper/2304.10547/) (2023)
- [The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producers](https://mlsanity.com/paper/2305.13238/) (2023)
- [Does AI-Assisted Fact-Checking Disproportionately Benefit Majority Groups Online?](https://mlsanity.com/paper/2302.03782/) (2023)
- [AI and Democracy's Digital Identity Crisis](https://mlsanity.com/paper/2311.16115/) (2023)

## Built on by (most cited first, 10 of 10)

- [Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking](https://mlsanity.com/paper/2502.09083/) (2025)
- [A Survey of Scaling in Large Language Model Reasoning](https://mlsanity.com/paper/2504.02181/) (2025)
- [The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizations](https://mlsanity.com/paper/2408.01962/) (2024)
- [Not Wrong, But Untrue: LLM Overconfidence in Document-Based Queries](https://mlsanity.com/paper/2509.25498/) (2025)
- [Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses](https://mlsanity.com/paper/2508.05009/) (2025)
- [Contradiction to Consensus: Dual Perspective, Multi Source Retrieval Based Claim Verification with Source Level Disagreement using LLM](https://mlsanity.com/paper/2602.18693/) (2026)
- [AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism](https://mlsanity.com/paper/2503.17401/) (2025)
- [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025)
- [Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool](https://mlsanity.com/paper/2507.06734/) (2025)
- [Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models](https://mlsanity.com/paper/2511.03699/) (2025)

## 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.