Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI

Autor:	Liu, Houjiang, Das, Anubrata, Boltz, Alexander, Zhou, Didi, Pinaroc, Daisy, Lease, Matthew, Lee, Min Kyung
Rok vydání:	2023
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Computation and Language Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at CSCW 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.07213 Zobrazit plný text záznamu View this record from Arxiv