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Seger and co-authors' paper of September 2023 argues that, although open-sourcing has historically brought net benefits, for some highly capable foundation models likely to be developed soon it may pose risks extreme enough to outweigh the benefits, partly because release is irreversible and safeguards can be removed. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
Supporting evidence: primary · 1 source
Subject version arXiv v1 (submitted 29 September 2023) · Published 2023-09-29 · Effective date not established Date 2023-09-29 · publication Records dated 2023 → Role Contesting Register (kind of text) Nonfiction Engagement Engages a premise. States the benefits attributed to openness and argues that removable safeguards and irreversibility outweigh them for highly capable models. Scope and limits The argument is limited to some future highly capable models; the paper recommends alternatives that preserve open-source benefits, and states that authorship does not entail endorsement of every claim.
The evidence 02 primary source Supports the claim
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives ↗ arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) · Publication date unknown
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Source excerpt “we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits.” Location in source Abstract, PDF p. 1
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8). arXiv v1, submitted 29 September 2023 (announced under identifier 2311.09227) Retrieved again at admission on 2026-10-30 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8, the same bytes). Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others), “Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives”. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, arguments about catastrophic risk from advanced AI (research/risk-debate). The quotation (27 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-31, item 74) re-read the record’s source and found the quotation and stance supported. No human editorial review. primary source Supports the claim
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives ↗ arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) · Publication date unknown
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Cite the original source Cite the original document, not Selekoa. Check these details against it.
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Source excerpt “open-source AI model release is irreversible; there is no “undo” function if significant harms materialize.” Location in source PDF p. 6
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8). arXiv v1, submitted 29 September 2023 (announced under identifier 2311.09227) Retrieved again at admission on 2026-10-30 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8, the same bytes). Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others), “Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives”. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, arguments about catastrophic risk from advanced AI (research/risk-debate). The quotation (15 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-31, item 74) re-read the record’s source and found the quotation and stance supported. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document disputes the proposition or a premise it depends on. The record documents the text’s position, not who is right.
Date. Date of publication, as the source gives it.
Record ID rd-r15-seger-2023-open-sourcing