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

Houjiang Liu; Anubrata Das; Alexander Boltz; Didi Zhou; Daisy Pinaroc; Matthew Lease; Min Kyung Lee

doi:10.1145/3686962

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arXiv · 2308.07213

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

Houjiang Liu·Anubrata Das·Alexander Boltz·Didi Zhou·Daisy Pinaroc·Matthew Lease·Min Kyung Lee

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Abstract

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.

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Houjiang Liu, Anubrata Das, Alexander Boltz, Didi Zhou, Daisy Pinaroc, Matthew Lease, Min Kyung Lee. 2024-10-14. Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI. https://doi.org/10.1145/3686962

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