Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
This addresses the problem of poor alignment between NLP tools and fact-checker practices, offering a human-centered approach for fact-checkers, designers, and researchers.
The paper tackled the limited adoption of NLP techniques in fact-checking by co-designing with professionals, resulting in 11 design ideas that integrate fact-checker criteria into novel NLP concepts.
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.