Handling and Presenting Harmful Text in NLP Research

Hannah Kirk, Abeba Birhane, Bertie Vidgen, Leon Derczynski


Abstract
Text data can pose a risk of harm. However, the risks are not fully understood, and how to handle, present, and discuss harmful text in a safe way remains an unresolved issue in the NLP community. We provide an analytical framework categorising harms on three axes: (1) the harm type (e.g., misinformation, hate speech or racial stereotypes); (2) whether a harm is sought as a feature of the research design if explicitly studying harmful content (e.g., training a hate speech classifier), versus unsought if harmful content is encountered when working on unrelated problems (e.g., language generation or part-of-speech tagging); and (3) who it affects, from people (mis)represented in the data to those handling the data and those publishing on the data. We provide advice for practitioners, with concrete steps for mitigating harm in research and in publication. To assist implementation we introduce HarmCheck – a documentation standard for handling and presenting harmful text in research.
Anthology ID:
2022.findings-emnlp.35
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
497–510
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.35
DOI:
10.18653/v1/2022.findings-emnlp.35
Bibkey:
Cite (ACL):
Hannah Kirk, Abeba Birhane, Bertie Vidgen, and Leon Derczynski. 2022. Handling and Presenting Harmful Text in NLP Research. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 497–510, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Handling and Presenting Harmful Text in NLP Research (Kirk et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.35.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.35.mp4