# A Critical Examination of the Ethics of AI-Mediated Peer Review

arXiv: https://arxiv.org/abs/2309.12356 · HTML: https://mlsanity.com/paper/2309.12356/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 116 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [The Challenge of Value Alignment: from Fairer Algorithms to AI Safety](https://mlsanity.com/paper/2101.06060/) (2021)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [Textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome](https://mlsanity.com/paper/1911.02648/) (2019)
- [The Arc of the Data Scientific Universe](https://mlsanity.com/paper/2102.10050/) (2021)

## Built on by (most cited first, 8 of 8)

- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future](https://mlsanity.com/paper/2604.27924/) (2026)
- [Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue](https://mlsanity.com/paper/2508.05283/) (2025)

## Abstract (arXiv)

Recent advancements in artificial intelligence (AI) systems, including large language models like ChatGPT, offer promise and peril for scholarly peer review. On the one hand, AI can enhance efficiency by addressing issues like long publication delays. On the other hand, it brings ethical and social concerns that could compromise the integrity of the peer review process and outcomes. However, human peer review systems are also fraught with related problems, such as biases, abuses, and a lack of transparency, which already diminish credibility. While there is increasing attention to the use of AI in peer review, discussions revolve mainly around plagiarism and authorship in academic journal publishing, ignoring the broader epistemic, social, cultural, and societal epistemic in which peer review is positioned. The legitimacy of AI-driven peer review hinges on the alignment with the scientific ethos, encompassing moral and epistemic norms that define appropriate conduct in the scholarly community. In this regard, there is a "norm-counternorm continuum," where the acceptability of AI in peer review is shaped by institutional logics, ethical practices, and internal regulatory mechanisms. The discussion here emphasizes the need to critically assess the legitimacy of AI-driven peer review, addressing the benefits and downsides relative to the broader epistemic, social, ethical, and regulatory factors that sculpt its implementation and impact.