1 research outputs found
Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
A key challenge in professional fact-checking is its limited scalability in
relation to the magnitude of false information. While many Natural Language
Processing (NLP) tools have been proposed to enhance fact-checking efficiency
and scalability, both academic research and fact-checking organizations report
limited adoption of such tooling due to insufficient alignment with
fact-checker practices, values, and needs. To address this gap, we investigate
a co-design method, Matchmaking for AI, which facilitates fact-checkers,
designers, and NLP researchers to collaboratively discover what fact-checker
needs should be addressed by technology and how. Our co-design sessions with 22
professional fact-checkers yielded a set of 11 novel design ideas. They assist
in information searching, processing, and writing tasks for efficient and
personalized fact-checking; help fact-checkers proactively prepare for future
misinformation; monitor their potential biases; and support internal
organization collaboration. Our work offers implications for human-centered
fact-checking research and practice and AI co-design research