Why Scholarly Publishing Needs a Neutral Governance Body for the AI Age
In response to the evolving landscape of scholarly communication, it has become clear that the mechanisms governing trust, correction, and authority established during the print and early digital eras are no longer sufficient. The integration of artificial intelligence (AI) systems into scholarly publishing has fundamentally altered how information is processed and disseminated.
The Structural Shift in Scholarly Communication
The scholarly communication system has crossed a structural threshold. In the past, issues such as retractions and corrections were manageable, with limitations that contained the potential for misinformation. However, in the current environment where AI can rapidly ingest and redistribute vast amounts of scholarly data, the situation has changed irreversibly. Once a scholarly article is included in a training corpus, it becomes part of a computational substrate that cannot be surgically edited, leading to widespread propagation of errors.
Governance Failures in the Scholarly Publishing Ecosystem
The existing scholarly publishing ecosystem has relied on a loose federation of publishers, libraries, and indexing services to manage trust. While organizations like Retraction Watch and Crossref have made significant contributions, their authority is often partial and not designed for machine governance. This inconsistency has resulted in a scenario where different actors define truth, trust, and reliability differently, which is untenable in an AI-driven research environment.
Leveraging Past Successes for Future Solutions
Historically, the scholarly community has demonstrated the ability to build global, neutral infrastructure when necessary. Initiatives like Crossref, ORCID, and COUNTER emerged in response to specific challenges, providing shared, machine-readable truth without ownership by any single publisher. AI now demands a similar institutional response focused on research integrity.
The Need for a Neutral Governance Layer
What is urgently needed is a neutral, nonprofit governance body dedicated to scholarly data in the AI era. This organization should have the authority to define, certify, and enforce standards regarding how scholarly content is labeled, transmitted, and utilized by machines.
Key responsibilities of this governance body would include:
- Retraction and Correction Signaling – Establishing an authoritative registry for retractions and corrections that is accessible via APIs and integrated into metadata pipelines.
- Provenance and Lineage Tracking – Creating machine-readable records detailing the origins and modifications of data and claims.
- Training-Data Certification – Developing standards for what scholarly content qualifies for AI training, including exclusions for retracted or ethically compromised material.
- Auditability and Transparency – Implementing frameworks that allow AI providers to certify dataset usage and filtering methods.
- Compliance and Enforcement – Establishing technical and reputational enforcement mechanisms akin to COUNTER compliance.
The Importance of Trust in the AI-Driven Future
As the landscape shifts, trust will not automatically transfer from journal brands to AI systems. Researchers will be more inclined to trust AI outputs from platforms that can demonstrate integrity. The future may belong to research institution-branded AI platforms that leverage clean, certified data pipelines.
The Consequences of Inaction
Without a shared governance infrastructure, the scholarly publishing landscape risks fragmentation. Each AI provider could define clean data differently, leading to inconsistent retraction signals and filtering processes. This fragmentation would hinder researchers’ ability to verify and trust results across systems, potentially damaging the credibility of AI-assisted science.
AI has not destroyed scholarly publishing; rather, it has prompted an urgent need for maturation. The challenge lies in whether the industry will respond by establishing robust governance or continue to rely on fragmented metadata and voluntary compliance.
