The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
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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“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
Citations
Cites
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Cited by
Sorted by citations
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050
“A.2 Full categorization: Motivations Allocational harms Hovy and Spruit (2016); Caliskan et al. (2017); Madnani et al. (2017); Dixon et al. (2018); Kiritchenko and Mohammad (2018); Shen et al. (2018); Zhao et al. (2018b); Bhaskaran and Bhallamudi (2019); Bordia and Bowman (2019); Brunet et al.…”
BackgroundInfluentialBender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854
“, 2017) and papers (Hovy and Spruit, 2016) in NLP, as well as workshops in related fields, notably the FATML series (http://www.”
BackgroundInfluentialSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“Thus, data, tools, and inferences obtained for one purpose can be used for another purpose (Hovy and Spruit, 2016; Benton et al., 2017); yet, the risks associated with secondary uses may not be well understood.”
BackgroundTaxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
“Choices on model architecture may also have an impact, as it has been proposed that the architecture of current LMs and particularly tokenisation is well-suited to English, but not morphologically more complex languages [14, 79, 162].”
BackgroundMeasuring and Mitigating Unintended Bias in Text Classification
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- social media data, ethical risks, public discourse on AI
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