---
title: "Explainable Automatic Fact-Checking for Journalists Augmentation in the Wild"
authors:
  - "Filipe Altoe"
  - "Sérgio Miguel Gonçalves Pinto"
  - "H Sofia Pinto"
doi: "10.24963/ijcai.2025/1140"
url: "https://ijcai.org/proceedings/2025/1140"
pdf: "https://ijcai.org/proceedings/2025/1140.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10262-10270"
track: "Human-Centred AI"
keywords:
  - "IJCAI25: Human-Centred AI"
---

# Explainable Automatic Fact-Checking for Journalists Augmentation in the Wild

Filipe Altoe, Sérgio Miguel Gonçalves Pinto, H Sofia Pinto

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10262-10270. DOI: [10.24963/ijcai.2025/1140](https://doi.org/10.24963/ijcai.2025/1140)

## Abstract

Journalistic manual fact-checking is the usual way to address fake news; however, this labor-intensive task regularly is not a match for the scale of the problem. The literature introduced automated fact-checking (AFC) as a potential solution; however, there is still missing functionality in the AFC pipeline, a lack of research benchmarking data, and a disconnect between their design and human factors crucial for adoption. We present a fully explainable AFC framework designed to augment professional journalists in the wild. A novel human annotation-free approach surpasses state-of-the-art multi-label classification by 12%. It is the first to demonstrate strong generalization across different claim subjects without retraining and to generate complete verdict explanation articles and their summaries. A focused user study of 103 professional journalists, with 93% having dedicated experience with fact-checking, validates the framework's level of explainability, transparency, and quality of generated fact-checking artifacts. The importance of establishing clear source selection and bias evaluation criteria reinforced the need for human augmentation, not replacement, by AFC systems.