以人为中心的 NLP 事实核查：使用 Matchmaking for AI 与事实核查员协同设计
Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
AI总结：
针对NLP事实核查技术因与实践需求脱节而采用率低的问题，研究采用Matchmaking for AI协同设计方法，与22名专业核查员合作产出了11项整合核查员标准的设计创意，为以人为中心的事实核查及AI协同设计提供了新见解。
摘要：
尽管已提出许多自然语言处理（NLP）技术用于事实核查，但学术研究和事实核查机构均报告称，由于与事实核查员的实践、价值观和需求契合度较差，此类 NLP 工作的采用率有限。为解决这一问题，我们研究了一种协同设计方法 Matchmaking for AI，使事实核查员、设计师和 NLP 研究人员能够共同确定哪些事实核查员需求应由技术解决，并为潜在解决方案构思创意。我们与 22 名专业事实核查员开展的协同设计会议产出了 11 项设计创意，提供了一个“北极星”，将事实核查员的标准整合到新颖的 NLP 设计概念中。这些概念涵盖预先防范错误信息、高效且个性化的错误信息监控、主动减少事实核查员的潜在偏见，以及协同撰写事实核查报告。我们的工作为以人为中心的事实核查研究与实践以及 AI 协同设计研究提供了新见解。
英文摘要：
While many Natural Language Processing (NLP) techniques have been proposed for fact-checking, both academic research and fact-checking organizations report limited adoption of such NLP work due to poor alignment with fact-checker practices, values, and needs. To address this, we investigate a co-design method, Matchmaking for AI, to enable fact-checkers, designers, and NLP researchers to collaboratively identify what fact-checker needs should be addressed by technology, and to brainstorm ideas for potential solutions. Co-design sessions we conducted with 22 professional fact-checkers yielded a set of 11 design ideas that offer a "north star", integrating fact-checker criteria into novel NLP design concepts. These concepts range from pre-bunking misinformation, efficient and personalized monitoring misinformation, proactively reducing fact-checker potential biases, and collaborative writing fact-check reports. Our work provides new insights into both human-centered fact-checking research and practice and AI co-design research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
