CoChat
Sign Up
Cited by 109Ethics and Social Impacts of AIAcademic Publishing and Open AccessScientific Computing and Data Management

Why open-source generative AI models are an ethical way forward for science

Open access
Spirling, Arthur·2023·Nature
DOI 10.1038/d41586-023-01295-4
Open this paper in CoChat — free accountOpen PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/d41586-023-01295-4

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Open-washing is bad for research, because it means that researchers can no longer count on being able to tinker with models and architectures even if they are advertised as open source.

    “Open-washing is also bad for research, because it means that researchers can no longer count on being able to tinker with models and architectures even if they are advertised as open source [54].”

    From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [54]

    The Abstract says that open-source LLMs let researchers inspect models and customize their code, and that this matters for reproducibility. It does not discuss “open-washing” or say that models advertised as open source prevent researchers from tinkering with them. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Generative AI may help increase external validity but may come at the cost of internal validity-the capacity of different groups to reproduce or replicate each other's results.

    “Though Generative AI may help researchers increase the external validity of their research designs, this may come at the cost of internal validity—or the capacity of different groups of researchers to reproduce or replicate each other’s results (2, 12).”

    From Can Generative AI improve social science? (2024) · cites this paper as [2]

  • If social scientists become reliant on proprietary models, they risk tying their endeavors to corporate interests.

    “If social scientists become reliant upon proprietary models, we also risk tying our endeavors to the vicissitudes of corporate interests (2).”

    From Can Generative AI improve social science? (2024) · cites this paper as [2]

  • The less black-box nature of open-source models can help researchers make the exact version used in their works available to others, thus facilitating transparency and reproducibility.

    “The less black-box nature of these open-source models can help researchers make the exact version used in their works available to others (100), thus facilitating transparency and reproducibility.”

    From Perils and opportunities in using large language models in psychological research (2024) · cites this paper as [100]

  • Even when updates address and mitigate detected biases, they also introduce the potential for process reproducibility failure in the generated data and impede reproducibility.

    “This means that even when updates address and mitigate detected biases, they also introduce the potential for “process reproducibility failure” (98) in the generated data and impede reproducibility, critical for scientific rigor (15, 100, 101).”

    From Perils and opportunities in using large language models in psychological research (2024) · cites this paper as [100]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Still being checked.

Cited by

Sorted by citations

Related

Authors
Spirling, A.
Affiliations
New York University (US)
Corresponding author
Spirling, Arthur
ORCID
Spirling, Arthur
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-04-18
Volume
616
Issue
7957
Pages
413-413
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/d41586-023-01295-4
OpenAlex
W4366277658
Cited by
104
References
0
Citations by year
2026: 23 · 2025: 32 · 2024: 28 · 2023: 20 · 2022: 1
FWCI
26.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Keywords
open-source models, generative AI, large language models, research ethics, reproducibility, transparency, scientific research
MeSH terms
Artificial Intelligence, Natural Language Processing, Research, Reproducibility of Results, Access to Information

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 ›

Are you an author, or do you see a problem with this page? Request a review