为自动事实核查工具设计可解释性说明，让核查员看得懂、信得过。
Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
- 通过访谈了解核查员如何评估证据与决策过程。
- 发现现有工具缺乏追踪推理路径与标注信息缺口的解释能力。
- 适合开发可信自动核查系统的研究人员和工具开发者。
大型语言模型和生成式AI在在线媒体中的普及加剧了对高效自动化事实核查的需求，以帮助核查人员应对日益增长且复杂的虚假信息。由于事实核查本身具有高度复杂性，自动化系统必须提供可解释性说明，使核查人员能够审查其输出结果。然而，目前尚不清楚这些说明应如何与核查人员的决策和推理过程对齐，才能有效融入工作流程。本研究通过半结构化访谈与事实核查专业人士，揭示了：(i) 核查员如何评估证据、做出判断并解释其过程；(ii) 核查员在实际中如何使用自动化工具；(iii) 自动化事实核查工具所需的具体解释要求。研究发现，当前存在未被满足的解释需求，提出可复现的解释应具备三个关键特征：追踪模型推理路径、引用具体证据、突出不确定性与信息空白。
原文摘要 · Abstract (English)
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.
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