Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
This addresses the challenge of integrating AI tools into fact-checking processes to combat misinformation, but it is incremental as it focuses on requirements rather than a new solution.
The study tackled the problem of aligning automated fact-checking systems with fact-checkers' workflows by identifying their explanation requirements through interviews, revealing unmet needs for replicable explanations that trace reasoning, reference evidence, and highlight uncertainty.
The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the increasing volume and sophistication of misinformation. The complex nature of fact-checking demands that automated fact-checking systems provide explanations that enable fact-checkers to scrutinise their outputs. However, it is unclear how these explanations should align with the decision-making and reasoning processes of fact-checkers to be effectively integrated into their workflows. Through semi-structured interviews with fact-checking professionals, we bridge this gap by: (i) providing an account of how fact-checkers assess evidence, make decisions, and explain their processes; (ii) examining how fact-checkers use automated tools in practice; and (iii) identifying fact-checker explanation requirements for automated fact-checking tools. The findings show unmet explanation needs and identify important criteria for replicable fact-checking explanations that trace the model's reasoning path, reference specific evidence, and highlight uncertainty and information gaps.