Show Me the Work: Fact-Checkers' Requirements for Explainable Automated
  Fact-Checking
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.
67b30726d4665a0448e6436d
Greta Warren
claimed_verified
2025-02-18T09:32:34.585Z
6698cffdb2ebada9f4a7e7d7
/avatars/e66d946c14595d3b008185f2be8d2f57.svg
Greta Warren
user
gretawarren
67b30726d4665a0448e6436e
Irina Shklovski
67b30726d4665a0448e6436f
Isabelle Augenstein
admin_assigned
2025-02-19T15:49:35.332Z
608918b7df398c3b285ce960
https://cdn-avatars.huggingface.co/v1/production/uploads/1621507769190-608918b7df398c3b285ce960.jpeg
Isabelle Augenstein
user
IAugenstein
Show Me the Work: Fact-Checkers' Requirements for Explainable Automated
  Fact-Checking
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.
Automated fact-checking systems need to provide explanations aligned with fact-checkers' decision-making processes, as identified through interviews with professionals.