Show Me the Work: Fact-Checkers' Requirements for Explainable Automated
  Fact-Checking
Authors: name: Greta Warren, name: Irina Shklovski, name: Isabelle Augenstein
Organization: CopeNLU

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.