A Paradigm for Democratizing Artificial Intelligence Research
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Avoiding AI monopolies would promote democratization of AI research and practice and decrease the risk of a singleton.
“This, in turn, would also promote the democratization of AI research and practice, as well as decrease the risk of the formation of a singleton [188,189].”
From The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities (2020) · cites this paper as [189]
The citation lists haven’t been fetched yet
References 32 · Cited by 10
- Authors
- Moreau, E., Vogel, C., & Barry, M.
- Affiliations
- Trinity College Dublin (IE)
- University College Dublin (IE)
- Corresponding author
- Moreau, Erwan
- Venue
- Intelligent Systems Reference Library
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 137-166
- ISSN
- 1868-4394 (ISSN-L)
- 1868-4408
- OpenAlex
- W2954777313
- Cited by
- 10
- References
- 32
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1
- FWCI
- 1.57
- Type
- book chapter
- Language
- en
- Open access
- green
- hal.science
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Intelligent systems reference library (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
- Keywords
- natural language processing, open source software development, language resources, NLP datasets, software tools, non-expert participation
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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