honest-scholar plugin (meta-spec §2.1) — the single most important guiding principle.
Status: Verified-source digest. Migrates to the plugin’s resources/references/.
This is a curated, primary-source digest. It exists so the agency principle is recorded as a grounded rule, not an opinion. All identifiers were verified at compile time except where flagged.
The principle
The honest-scholar skills are assistants, not researchers. They keep the
accounts of a research program, advise as a mentor, and discuss as a colleague,
but they do not perform independent research and do not make material
scientific decisions. Every material decision — is a hypothesis confirmed /
refuted; is a result real; is a paper worth publishing; what the thesis claims;
is it defensible — is the human researcher’s, recorded with a named human
sign-off. You author; the skill drafts. Automation covers the mechanical and
mnemonic, never the judgemental. The researcher is in the driving seat.
It is not an anti-AI stance. Every norm below permits AI assistance
(editing, retrieval, bookkeeping, discussion) while forbidding AI authorship
and AI decision-making. The principle draws that bright line and keeps the
skills useful on the permitted side while structurally refusing to cross it.
Why it holds — grounded
1. Authorship & accountability: accountability is non-delegable and attaches only to humans
- ICMJE — authorship requires final approval and accountability; AI tools cannot be authors because they cannot take responsibility, declare conflicts, or hold copyright; humans remain responsible for AI-produced content.
- COPE position statement, “Authorship and AI tools” (13 Feb 2023) — AI cannot be listed as an author; use must be disclosed; authors are fully responsible.
- Nature/Springer Nature — LLMs fail authorship criteria because “authorship carries accountability… which cannot be effectively applied to LLMs.”
- Science/AAAS — Thorp, “ChatGPT is fun, but not an author,” Science 379(6630):313 (2023); undisclosed AI-generated text treated as plagiarism.
- Elsevier / Wiley / T&F / SAGE — same consensus: only humans/legal persons can hold accountability.
- ML venues — ICML 2023: text produced entirely by LLMs prohibited (except as experimental object); polishing author text allowed; the human is “ultimately responsible.” NeurIPS: LLMs allowed as tools but must be described; responsibility stays with authors. ACL 2023: language/search assistance OK, but authors must read/verify every reference; errors and plagiarism are the author’s responsibility.
- CRediT (ANSI/NISO Z39.104-2022) — 14 contributor roles attribute contribution to named people, not tools.
2. Human oversight / human-in-command: decision authority stays human
- EU AI Act, Article 14 (human oversight) — high-risk AI must be designed for oversight by natural persons who can interpret output, override it, or decide not to use it, and must “remain aware of… over-relying on the output (automation bias).” Ties oversight directly to automation bias.
- Santoni de Sio & van den Hoven (2018), “Meaningful Human Control over Autonomous Systems,” Frontiers in Robotics and AI 5:15 — grounds control in tracking (responsive to human reasons) and tracing (a human who understands and is answerable). “Recorded with a human sign-off” is the tracing condition made concrete.
3. Automation bias: why the skill must halt and ask, not decide
- Parasuraman & Riley (1997), “Humans and Automation: Use, Misuse, Disuse, Abuse,” Human Factors 39(2):230–253 — canonical taxonomy; “misuse” = over-reliance causing monitoring failure and decision bias.
- Skitka, Mosier & Burdick (1999), Int. J. Human-Computer Studies 51(5):991–1006 — empirical automation bias: people accept incorrect automated advice over available contradictory evidence.
- Research-specific failure mode — trusting an LLM to decide (vs assist) injects fabricated citations; audits report high fabrication rates in LLM-generated references. (Some figures come from 2026 preprints — treat as illustrative; verify final-venue status before formal use.)
4. Research-integrity frameworks place accountability on the researcher
- Singapore Statement on Research Integrity (2010) — four principles: honesty, accountability, professionalism, stewardship.
- ALLEA, European Code of Conduct for Research Integrity (Revised 2023) — the EU’s primary integrity standard; the 2023 revision addresses digital/AI tools and places responsibility for good practice on researchers.
5. “Augment, not replace” — the positive framing
- Engelbart (1962), “Augmenting Human Intellect: A Conceptual Framework” (SRI AFOSR-3223) — tools amplify human capability within a human-directed system.
- Bush (1945), “As We May Think,” The Atlantic — the memex aids memory and association, not judgement.
Guardrails that follow (design-enforceable)
- Named human sign-off on every material decision (hypothesis verdict, result-is-real, publishable, thesis claim, defensibility). Record the human, not the skill. (Singapore/ALLEA accountability + ICMJE final-approval + meaningful-human-control “tracing”.)
- Skill halts at judgement points and asks. The human must be able to disregard the tool. (EU AI Act Art. 14; automation-bias literature.)
- Drafts are proposals, never authored text. The researcher reads, verifies, and adopts. (ICMJE/COPE/Science/Nature.)
- No unattended end-to-end paper/thesis generation. (Science = plagiarism; ICML = prohibited.)
- Verify every AI-surfaced reference before use. (ACL 2023; fabrication rates.)
- Disclose and log AI use (the mechanical/mnemonic assistance) — feeds the audit trail the skills already maintain. (COPE/Nature/ICML/NeurIPS.)
- Automate only the mechanical and mnemonic — bookkeeping, formatting, retrieval, roll-ups, reminders; never the judgemental.
The bright line (nuance to preserve)
Every policy cited permits AI assistance and forbids AI authorship / decision-making. The principle is that line, not a ban. One caveat: the “editing/polishing without disclosure” carve-out (Nature) sits close to the line —honest-scholar should default to disclosure to stay clearly on the safe
side.
Sources
- ICMJE — Role of Authors and Contributors: https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html; AI use by authors: https://www.icmje.org/recommendations/browse/artificial-intelligence/ai-use-by-authors.html
- COPE — Authorship and AI tools (2023-02-13): https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools
- Nature Portfolio — AI policy: https://www.nature.com/nature-portfolio/editorial-policies/ai
- Thorp, H.H. (2023), Science 379(6630):313, DOI 10.1126/science.adg7879
- Elsevier publishing-ethics: https://www.elsevier.com/about/policies-and-standards/publishing-ethics (not directly fetched; corroborated via secondary sources)
- ICML 2023 LLM policy: https://icml.cc/Conferences/2023/llm-policy
- NeurIPS LLM policy: https://neurips.cc/Conferences/2025/LLM
- ACL 2023 Policy on AI Writing Assistance: https://2023.aclweb.org/blog/ACL-2023-policy/
- CRediT (ANSI/NISO Z39.104-2022): https://credit.niso.org · https://www.niso.org/publications/z39104-2022-credit
- EU AI Act Art. 14 (Human oversight): https://artificialintelligenceact.eu/article/14/
- Santoni de Sio & van den Hoven (2018), Front. Robot. AI 5:15, DOI 10.3389/frobt.2018.00015
- Parasuraman & Riley (1997), Human Factors 39(2):230–253, DOI 10.1518/001872097778543886
- Skitka, Mosier & Burdick (1999), IJHCS 51(5):991–1006, DOI 10.1006/ijhc.1999.0252
- Singapore Statement on Research Integrity (2010): https://wcrif.org/guidance/singapore-statement
- ALLEA European Code of Conduct for Research Integrity (2023): https://allea.org/code-of-conduct/
- Engelbart (1962), Augmenting Human Intellect: https://www.dougengelbart.org/pubs/augment-3906.html
- Bush (1945), As We May Think, The Atlantic (canonical; not re-fetched)