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.
BibTeX
@inproceedings{subramonian-etal-2024-understanding,
title = "Understanding {\textquotedblleft}Democratization{\textquotedblright} in {NLP} and {ML} Research",
author = "Subramonian, Arjun and
Gautam, Vagrant and
Klakow, Dietrich and
Talat, Zeerak",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.184/",
doi = "10.18653/v1/2024.emnlp-main.184",
pages = "3151--3166"
}