Derivative Epistemic Agency: AI-Mediated Scientific Cognition and the Limits of Human Answerability
Generative AI can assist in scientific work, while leaving authors responsible for claims they may not adequately understand. Existing authorship and disclosure frameworks establish human accountability, but declaring supervision does not demonstrate the competence required to exercise it. This conceptual viewpoint examines when reliance on generative AI becomes incompatible with scientific answerability and proposes a proportionate response for biomedical publishing. The analysis distinguishes integrated epistemic extension from derivative epistemic substitution along a graded continuum of origination, comprehension, evaluation, substantive answerability, control, provenance awareness, and competence retention. Origination alone is not disqualifying: researchers may responsibly adopt ideas suggested by systems or collaborators. The central requirement is sufficient justificatory competence, assessed through the ability to address reasonable specialist challenges concerning evidence, assumptions, limitations, alternatives, and possible revision. This requirement applies across the author team and scales with the importance of the claim and the consequences of error. It does not require reproducing every cognitive operation, explaining model internals, or abandoning justified reliance on independently established reliability. Responsibility asymmetry arises when authors retain formal responsibility, AI supplies substantial inferential content, and the team lacks adequate coverage of the capacities needed to justify adopting that content. The problem is, therefore, not the proportion of generated text. A single unsupported inference can matter more than extensive language assistance. Work on distributed cognition, scientific understanding, automation, and research integrity supports background analysis. Evidence concerning cognitive offloading and possible deskilling is discussed with explicit limits: observational clinical findings and self-reported knowledge work cannot establish that generative AI causes lasting competence loss among scientists. Institutional incentives are treated as a possible reinforcing mechanism rather than an established causal explanation. The viewpoint compares authorship criteria, contributor roles, methodological attribution, and recent AI reporting frameworks, including GAIDeT (Generative Artificial Intelligence Delegation Taxonomy) and GAMER (Generative Artificial Intelligence Tools in Medical Research). These instruments offer complementary forms of transparency and verification, but reporting activities does not itself establish retained capacity to defend a consequential inference. Proposed extensions include an epistemic participation profile linking delegated operations to named authors; reporting at the manuscript, operation, or claim level according to risk; and targeted reasoning audits triggered by unresolved concerns. Audits should assess substance; permit language assistance and accommodations; and provide confidentiality, opportunities to respond, and appeal. Remediation should prioritize restoring or supplementing competence, narrowing claims, or declining to adopt unsupported conclusions. These instruments are normative prototypes, not validated measures or established editorial requirements. Pilots should evaluate feasibility, agreement between assessors, erroneous concerns, successful remediation, and unequal effects across researcher groups. The framework preserves legitimate collaboration and linguistic assistance while identifying a question that disclosure alone cannot settle: whether human authors can justify the scientific claims they adopt. Its contribution is an operational specification of answerability rather than a general objection to technologically distributed cognition.
Published in J Meta and Res Int, 2026
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