Understanding "Democratization" in NLP and ML Research
- URL: http://arxiv.org/abs/2406.11598v2
- Date: Sat, 05 Oct 2024 20:24:47 GMT
- Title: Understanding "Democratization" in NLP and ML Research
- Authors: Arjun Subramonian, Vagrant Gautam, Dietrich Klakow, Zeerak Talat,
- Abstract summary: 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.
We call for researchers to enrich their use of the term democratization with appropriate theory, towards democratic technologies beyond superficial access.
- Score: 22.061018815342184
- License:
- 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.
Related papers
- Can LLMs advance democratic values? [0.0]
We argue that LLMs should be kept well clear of formal democratic decision-making processes.
They can be put to good use in strengthening the informal public sphere.
arXiv Detail & Related papers (2024-10-10T23:24:06Z) - From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis [48.14390493099495]
This paper examines the governance of large language models (MM-LLMs) through individual and collective deliberation.
We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI.
arXiv Detail & Related papers (2024-09-15T03:17:38Z) - Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics.
Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues.
The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
arXiv Detail & Related papers (2024-03-15T04:02:24Z) - Enabling the Digital Democratic Revival: A Research Program for Digital
Democracy [68.02254954746476]
This white paper outlines a long-term scientific vision for the development of digital-democracy technology.
It arose from the Lorentz Center Workshop on Algorithmic Technology for Democracy'' (Leiden, October 2022)
arXiv Detail & Related papers (2024-01-30T10:12:49Z) - Embedding Democratic Values into Social Media AIs via Societal Objective
Functions [13.903836222333977]
We introduce a method for translating established, vetted social scientific constructs into AI objective functions.
We create a democratic attitude model that estimates the extent to which a social media post promotes anti-democratic attitudes.
This method presents a novel strategy to draw on social science theory and methods to mitigate societal harms in social media AIs.
arXiv Detail & Related papers (2023-07-26T02:27:24Z) - Democratization of Quantum Technologies [0.0]
Democratization is mainly adopted by companies working on quantum computing and used in a very narrow understanding of the concept.
We argue that more reflexivity and responsiveness regarding the narratives and actions adopted by the actors in the QT field, and making the underlying assumptions of ongoing efforts on democratization of QT explicit, can result in a better technology for society.
arXiv Detail & Related papers (2022-08-05T19:23:09Z) - Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
Deep learning has been the mainstream technique in natural language processing (NLP) area.
Deep learning requires many labeled data and is less generalizable across domains.
Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
arXiv Detail & Related papers (2022-05-03T13:58:38Z) - Explainable Patterns: Going from Findings to Insights to Support Data
Analytics Democratization [60.18814584837969]
We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings.
ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
arXiv Detail & Related papers (2021-01-19T16:13:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.