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

TL;DR

This paper presents a co-design approach called Matchmaking for AI, involving fact-checkers and NLP researchers to develop AI tools aligned with fact-checker needs, resulting in 11 innovative design ideas.

Contribution

It introduces a collaborative co-design method that bridges the gap between NLP technology and fact-checker practices, generating practical AI concepts for fact-checking.

Findings

Identified 11 design ideas for fact-checking AI tools

Facilitated collaboration between fact-checkers and NLP researchers

Provided insights into human-centered fact-checking and AI co-design

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.