[Paper] A data-centric approach for ethical and trustworthy AI in journalism

[Paper] Screens as Battlefields: Fact-Checkers’ Multidimensional Challenges in Debunking Russian-Ukrainian War Propaganda

[Conference] Dealing with biases and hallucinations: The ethical uses of (G)AI tools in the European news media sector

[Conference] Artificial intelligence and journalism: guidelines for ensuring information quality

[Preprint (accepted)] News Aggregation

[Paper] Striking the Balance in Using LLMs for Fact-Checking: A Narrative Literature Review

[Paper] Debunking war information disorder: A case study in assessing the use of multimedia verification tools

This paper investigates the use of multimedia verification, in particular, computational tools and Open-source Intelligence (OSINT) methods, for verifying online multimedia content in the context of the ongoing wars in Ukraine and Gaza.

[Paper] Transparency and fact-checking in open societies

Transparency is seen as a means to achieve accountability and credibility in reporting and as a tool to hold public figures accountable. However, transparency does not protect Nordic fact-checkers from criticism or harassment for delivering uncomfortable truths. Transparency is not without flaws, even in societies characterised by a culture of openness… and transparency.

[Chapter] From bytes to bylines – A history of AI in journalism practices

This chapter is published as part of Histories of Digital Journalism (2024) and explores the historical development of artificial intelligence (AI) in journalism. Highlighting the ethical complexities and professional challenges associated with integrating AI into newsrooms, it underscores journalism’s ability to adapt and engage with evolving technologies.

From AI to foreign influence: How European fact-checkers are fighting disinformation

The first conference of the European Fact-Checking Standards Network (EFCSN) took place in Brussels, marking a significant milestone for European fact-checkers as they unite to tackle the rapidly evolving challenges of disinformation.

[Tutorial] Talking to journalists about AI

AI is testing the boundaries of human intelligence. AI will shape your soul. This AI really wants to know you. AI loves you. We still know little about how AI thinks. AI is racist and sexist. AI lies. AI does not want to be regulated. AI doesn’t care about you. AI cooks perfect steaks.

20 sets of tasks for using large language models (LLMs) in journalism

This list ilustrates the potential for using Large Language Models (LLMs) in journalism, and how they can be used under human supervision.

[Paper] Outsourcing, Augmenting, or Complicating: The Dynamics of AI in Fact-Checking Practices in the Nordics

Results show that while AI technologies offer valuable functionalities, fact-checkers remain critical and cautious, particularly toward AI, due to concerns about accuracy and reliability. Despite acknowledging the potential of AI to augment human expertise and streamline specific tasks, these concerns limit its wider use. AI and GAI-based solutions are framed as “enablers” rather than comprehensive or end-to-end solutions, recognizing their limitations in replacing or augmenting complex human cognitive skills.