AI evidence extraction

Beyond AI disclosure: Claim accountability and responsible research in scholarly publishing

Ward van Zoonen, Aizhan Tursunbayeva, Anna Morgan‐Thomas · European Management Journal · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.emj.2026.06.001

Methodology & findings

Study design

Conceptual framework development and policy analysis.

Main result

The paper argues that "the right unit of governance is the claim, not the tool" and that "Responsible research with AI requires a named human who can reconstruct and defend each claim that enters the scholarly record." Current journal and publisher policies have "largely responded to AI through prohibition, mandatory disclosure, or validity-only permissiveness" but these approaches "regulates AI use in the production of manuscripts rather than the defensibility of scholarly claims, leaving the central accountability problem intact."

Research paradigm

Interpretive/argumentative - normative ethics and institutional analysis

Author conclusions

The authors conclude that "AI is the stress test of scholarly publishing. It has exposed weaknesses in how we evaluate, produce, and protect knowledge, and no gain in efficiency justifies a published claim that no human can defend." They advocate for a two-threshold framework centered on claim accountability rather than tool regulation, requiring role-based self-assessment across authorship, review, editing, and publishing.

Open questions raised

The paper identifies gaps in current governance approaches to AI in scholarly publishing, noting that existing policies focus on tools rather than claims, and proposes institutional mechanisms to address accountability deficits.
The paper identifies gaps in how scholarly publishing currently handles AI-assisted research, specifically the lack of accountability mechanisms tied to individual claims rather than tool usage, and the need for institutional anchors to prevent accountability frameworks from becoming boilerplate.
The paper identifies gaps in current scholarly publishing governance: existing journal and publisher policies have "largely responded to AI through prohibition, mandatory disclosure, or validity-only permissiveness," leaving the accountability problem unresolved.

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Responsible AI Use Research Integrity Conceptual