Friday, 20th October - 11am Michael Schlichtkrull : Seminar

Title: Taking Automated Fact-checking to the Real World

Abstract:

Automated fact-checking is typically presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation. Nevertheless, most research papers are surprisingly vague about how this technology is to be used. In the first part of this talk, I will argue that vague arguments about intended use hinders research in the area. I will use content analysis of highly cited papers to document and clarify the problem, and to establish recommendations. In the second part, I will try to follow my own recommendations, as I introduce a new dataset for automated fact-checking. I propose that human fact-checking is an effective method for fighting misinformation, and accordingly attempt to reverse-engineer the process. The resulting dataset allows reasoning about the capacity for models – including LLMs – to help human fact-checkers with some or all of their real-world fact-checking tasks.

Bio:

Michael Schlichtkrull is an affiliated lecturer and postdoctoral research associate at the University of Cambridge, where he works on automated fact-checking and other epistemically complicated NLP problems. Michael received his PhD from the University of Amsterdam, while also spending several years as a visitor at the University of Edinburgh. He worked on graph neural networks for NLP, tackling problems including relational link prediction, question answering, and interpretability.

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