[Paper Review] A Critical Examination of the Ethics of AI-Mediated Peer Review

Laurie A. Schintler, Connie L. McNeely+1 more|arXiv (Cornell University)|Sep 2, 2023

Artificial Intelligence in Healthcare and Education9 citations

TL;DR

The paper critically analyzes the ethics of AI-mediated peer review, arguing for alignment with moral, epistemic, and regulatory norms and examining norm-counternorm dynamics in institutional contexts.

ABSTRACT

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.

Motivation & Objective

• Assess the ethical and epistemic implications of using AI in peer review.
• Evaluate how AI-mediated review aligns with scientific norms and the broader scholarly ecosystem.
• Examine normative frameworks and institutional logics that influence acceptability and governance of AI in peer review.

Proposed method

• The authors provide a normative and conceptual examination of ethics in AI-mediated peer review.
• They discuss the concept of a norm-counternorm continuum to understand legitimacy in context.
• They analyze the benefits, downsides, and regulatory considerations without empirical experimentation.
• They situate AI review within broader epistemic, social, cultural, and societal factors.

Experimental results

Research questions

• RQ1 What ethical and epistemic norms should govern AI-mediated peer review?
• RQ2 How do institutional logics and regulatory mechanisms shape the acceptability of AI in peer review?
• RQ3 What are the potential benefits and harms of AI-driven review relative to traditional peer review?
• RQ4 How can legitimacy of AI-mediated peer review be evaluated within the scientific ethos?

Key findings

• AI-mediated peer review presents promise for efficiency but raises ethical and social concerns that could affect integrity.
• Human peer review also suffers from biases, abuses, and transparency issues, complicating simple risk-benefit judgments.
• Legitimacy depends on alignment with moral and epistemic norms and is influenced by institutional practices and governance.
• A norm-counternorm continuum shapes how AI in peer review is perceived and adopted across contexts.