The social impact of natural language processing
Open accessAbstract
Medical sciences have long since established an ethics code for experiments, to minimize the risk of harm to subjects.Natural language processing (NLP) used to involve mostly anonymous corpora, with the goal of enriching linguistic analysis, and was therefore unlikely to raise ethical concerns.As NLP becomes increasingly wide-spread and uses more data from social media, however, the situation has changed: the outcome of NLP experiments and applications can now have a direct effect on individual users' lives.Until now, the discourse on this topic in the field has not followed the technological development, while public discourse was often focused on exaggerated dangers.This position paper tries to take back the initiative and start a discussion.We identify a number of social implications of NLP and discuss their ethical significance, as well as ways to address them.
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Data, tools, and inferences obtained for one purpose can be used for another purpose.
“Thus, data, tools, and inferences obtained for one purpose can be used for another purpose (Hovy and Spruit, 2016; Benton et al., 2017)”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Hovy and Spruit, 2016
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How AI tools can—and cannot—help organizations become more ethical
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TextHide: Tackling Data Privacy in Language Understanding Tasks
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Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals
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Examining Gender and Race Bias in Two Hundred Sentiment Analysis SystemsPreprint
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An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding
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Conceptor Debiasing of Word Representations Evaluated on WEAT
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On the Machine Learning of Ethical Judgments from Natural Language
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A Quantitative Study of Data in the NLP community
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- Authors
- Hovy, D., & Spruit, S. L.
- Affiliations
- University of Copenhagen (DK)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2016
- Pages
- 591-598
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2511234952
- Cited by
- 329
- References
- 53
- Citations by year
- 2026: 31 · 2025: 39 · 2024: 18 · 2023: 47 · 2022: 45 · 2021: 58 · 2020: 40 · 2019: 16 · 2018: 17 · 2017: 15 · 2016: 3
- FWCI
- 71.55
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- social media data, ethical risks, public discourse on AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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