[论文解读] 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 Education被引用 9

一句话总结

该论文批判性分析了AI中介的同行评审伦理，主张与道德、认识论与监管规范保持一致，并考察制度环境中的规范-反规范动态。

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.

研究动机与目标

• 评估在同行评审中使用AI的伦理和认识论影响。
• 评估AI中介评审如何与科学规范和更广泛的学术生态系统保持一致。
• 考察影响同行评审可接受性与治理的规范框架和制度逻辑。

提出的方法

• 作者对AI中介同行评审的伦理进行规范性和概念性检验。
• 他们讨论规范-反规范的连续体概念，以在情境中理解合法性。
• 在不进行实证实验的前提下分析利弊与监管考量。
• 将AI评审置于更广泛的认识论、社会、文化和社会因素之中。

实验结果

研究问题

• RQ1 哪些伦理与认识论规范应当规制AI中介的同行评审？
• RQ2 制度逻辑与监管机制如何影响AI在同行评审中的可接受性？
• RQ3 与传统同行评审相比，AI驱动评审的潜在利益与危害有哪些？
• RQ4 如何在科学精神中评估AI中介同行评审的合法性？

主要发现

• AI中介的同行评审在提高效率方面具有潜力，但也会带来可能影响公正性的伦理与社会关注点。
• 人工评审也存在偏见、滥用和透明度问题，使简单的风险-收益判断变得复杂。
• 合法性取决于与道德与认识论规范的一致性，并受制度做法与治理的影响。
• 规范-反规范连续体塑造了在不同情境下人们对AI在同行评审中的看法与采用方式。