Understanding “Democratization” in NLP and ML Research

Arjun Subramonian, Vagrant Gautam, Dietrich Klakow, Zeerak Talat


Abstract
Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the “democratization” of artificial intelligence. In this paper, we seek to clarify how democratization is understood in NLP and ML publications, through large-scale mixed-methods analyses of papers using the keyword “democra*” published in NLP and adjacent venues. We find that democratization is most frequently used to convey (ease of) access to or use of technologies, without meaningfully engaging with theories of democratization, while research using other invocations of “democra*” tends to be grounded in theories of deliberation and debate. Based on our findings, we call for researchers to enrich their use of the term democratization with appropriate theory, towards democratic technologies beyond superficial access.
Anthology ID:
2024.emnlp-main.184
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3151–3166
Language:
URL:
https://aclanthology.org/2024.emnlp-main.184
DOI:
10.18653/v1/2024.emnlp-main.184
Bibkey:
Cite (ACL):
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow, and Zeerak Talat. 2024. Understanding “Democratization” in NLP and ML Research. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 3151–3166, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Understanding “Democratization” in NLP and ML Research (Subramonian et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.184.pdf