Veracity: An Open-Source AI Fact-Checking System

2025/06/18 by Taylor Lynn Curtis, Maximilian Puelma Touzel, Curtis, Taylor Lynn +19 · 1 voice · 1 citation
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Misinformation and Its Impacts #cs.AI #cs.CL #cs.HC

paper · pdf · doi:10.48550/arxiv.2506.15794

openalex publication_date 2025/06/18 · arxiv published 2025/06/18 · arxiv updated 2025/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

Citations

• CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit
• Web Retrieval Agents for Evidence-Based Misinformation Detection
• Long-form factuality in large language models
• Correcting misinformation on social media with a large language model
• Exploiting Novel GPT-4 APIs
• Factuality Challenges in the Era of Large Language Models
• Factuality challenges in the era of large language models and opportunities for fact-checking
• High level of correspondence across different news domain quality rating sets
• Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
• A Review on Fact Extraction and Verification

Cited by

• Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection

Discussions

• For no clear reason, @theguardian.com neglects to link to the relevant paper. This seems to be the missing paper: arxiv.org/abs/2506.15794 [bsky, 0 points, 0 comments]

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