Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915076404936704 |
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| author | Liu, Houjiang Das, Anubrata Boltz, Alexander Zhou, Didi Pinaroc, Daisy Lease, Matthew Lee, Min Kyung |
| author_facet | Liu, Houjiang Das, Anubrata Boltz, Alexander Zhou, Didi Pinaroc, Daisy Lease, Matthew Lee, Min Kyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07213 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI Liu, Houjiang Das, Anubrata Boltz, Alexander Zhou, Didi Pinaroc, Daisy Lease, Matthew Lee, Min Kyung Human-Computer Interaction Computation and Language Computers and Society 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. |
| title | Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI |
| topic | Human-Computer Interaction Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2308.07213 |