AI in Peer Review: Heuristics for Academic Publishers

1. Technical Architecture & Performance

1.1 Model Transparency

Can the AI system's decision-making process be inspected, explained, and validated by domain experts?

1.2 Performance Boundaries

Are the system's capabilities and limitations explicitly defined and empirically validated for peer review tasks?

1.3 Reliability Metrics

What quantitative evidence demonstrates consistent performance across diverse manuscript types, disciplines, and edge cases?

1.4 Interoperability

How and where does the system integrate with existing editorial management systems and workflows?

2. Data Governance & Privacy

2.1 Confidentiality Protocols

How is manuscript content isolated from model training pipelines, external data repositories, and other 3rd party purposes?

2.2 Intellectual Property Protection

What contractual and technical safeguards prevent unauthorised use or exposure of research data?

2.3 Data Retention & Deletion

What policies govern the storage, access, and disposal of processed manuscripts?

2.4 Compliance Framework

How does the system comply with frameworks such as privacy legislations, institutional review board policies, and disciplinary data standards?

3. Risk Management & Quality Assurance

3.1 Adversarial Resilience

What defences exist against prompt injection, model manipulation, and systematic gaming?

3.2 Hallucination Prevention

How are fabricated references, false claims, and spurious correlations detected and prevented?

3.3 Bias Mitigation

What processes identify and correct for demographic, geographic, institutional, and methodological biases?

3.4 Error Recovery

What protocols exist for identifying, documenting, and rectifying AI-generated errors post-publication?

4. Governance & Accountability Structure

4.1 Liability Framework

Who bears legal and ethical responsibility for AI-generated review content?

4.2 Decision Authority

At what point in a process is human judgment mandatory versus optional?

4.3 Audit Infrastructure

What documentation trail enables post-hoc review of AI involvement in editorial decisions?

4.4 Escalation Pathways

How are disputes, appeals, and concerns about AI use handled?

5. Stakeholder Impact & Communication

5.1 Disclosure Standards

How is AI involvement communicated to authors, reviewers, readers, and indexing services?

5.2 Consent Mechanisms

What opt-in or opt-out options exist for authors and reviewers?

5.3 Feedback Loops

How is stakeholder experience regularly captured and incorporated into system improvements?

6. Economic & Strategic Considerations

6.1 Cost-Benefit Analysis

Beyond efficiency gains, what value does AI add to review quality and journal reputation?

6.2 Vendor Dependencies

What contingencies exist if an AI provider discontinues service, changes terms, increases prices, or alters service quality?

6.3 Competitive Positioning

How does AI adoption affect the journal's reputational standing, relative to peers?

6.4 Resource Allocation

What human and technical resources are required for responsible well-governed implementation?

7. Scholarly Ecosystem & Long-term Sustainability

7.1 Reviewer Development

How might AI use affect early-career researcher training and expertise development?

7.2 Community Trust

What evidence demonstrates that AI enhances rather than undermines scholarly credibility?

7.3 Knowledge Evolution

How does the system adapt to emerging methodologies, interdisciplinary work, and paradigm shifts?

7.4 Cultural Preservation

What measures ensure AI augments rather than replaces collegial discourse and mentorship?

8. Validation & Continuous Improvement

8.1 Independent Verification

Has the system undergone third-party evaluation specific to peer review contexts?

8.2 Performance Monitoring

What metrics track accuracy, fairness, and stakeholder satisfaction?

8.3 Update Protocols

How are model improvements tested and deployed without disrupting ongoing publishing and peer review processes?

8.4 Sunset Criteria

What triggers would necessitate discontinuing or fundamentally restructuring AI use?

About this Document

AI in academic publishing is often sold as a story of speed and efficiency. But behind the hype, there are major gaps: few practical tools help publishers weigh the risks, trade-offs, and cultural implications of using AI in peer review.

This document is an sketch framework of heuristics — not a finished guide, but a set of questions drawn from my experience and observations that highlight where careful scrutiny is most needed.

I hope you find it useful.

How to use it

Treat this less as a checklist and more as a set of provocations.

Each heuristic is framed as a question for publishers and institutions. Some will apply directly; others may spark new ideas and directions. The aim isn’t to settle the debate, but to widen it.

Current state

This is an early draft, incomplete and imperfect. I’m sharing it now to test it in the open, rather than waiting for a “finished” version that may never come.

How to contribute

This framework, if useful can eventually live somewhere more structured — a shared space where others can contribute, adapt, or challenge it. For now, I’m releasing it in this rough form to start the conversation.

If you’ve got thoughts, critiques, or additions, you can comment on this document, or can reach me directly on LinkedIn or Bluesky . I'm eager to hear from you how these heuristics resonate, where they fall short, and what’s missing.

— Barry

License

AI in Peer Review: Heuristics for Academic Publishers © 2025 by Barry Prendergast is licensed under CC BY-NC-SA 4.0