The AI Imperative: Scaling High-Quality Peer Review in Machine Learning
- URL: http://arxiv.org/abs/2506.08134v2
- Date: Wed, 18 Jun 2025 23:48:35 GMT
- Title: The AI Imperative: Scaling High-Quality Peer Review in Machine Learning
- Authors: Qiyao Wei, Samuel Holt, Jing Yang, Markus Wulfmeier, Mihaela van der Schaar,
- Abstract summary: We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
- Score: 49.87236114682497
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the finite capacity of qualified reviewers, leading to concerns about review quality, consistency, and reviewer fatigue. This position paper argues that AI-assisted peer review must become an urgent research and infrastructure priority. We advocate for a comprehensive AI-augmented ecosystem, leveraging Large Language Models (LLMs) not as replacements for human judgment, but as sophisticated collaborators for authors, reviewers, and Area Chairs (ACs). We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making. Crucially, we contend that the development of such systems hinges on access to more granular, structured, and ethically-sourced peer review process data. We outline a research agenda, including illustrative experiments, to develop and validate these AI assistants, and discuss significant technical and ethical challenges. We call upon the ML community to proactively build this AI-assisted future, ensuring the continued integrity and scalability of scientific validation, while maintaining high standards of peer review.
Related papers
- When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs [8.575522204707958]
Large language models (LLMs) grow in capability and autonomy, evaluating their outputs-especially in open-ended and complex tasks-has become a critical bottleneck.<n>A new paradigm is emerging: using AI agents as the evaluators themselves.<n>In this review, we define the agent-as-a-judge concept, trace its evolution from single-model judges to dynamic multi-agent debate frameworks, and critically examine their strengths and shortcomings.
arXiv Detail & Related papers (2025-08-05T01:42:25Z) - On Benchmarking Human-Like Intelligence in Machines [77.55118048492021]
We argue that current AI evaluation paradigms are insufficient for assessing human-like cognitive capabilities.<n>We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically-invalid tasks.
arXiv Detail & Related papers (2025-02-27T20:21:36Z) - ReviewEval: An Evaluation Framework for AI-Generated Reviews [9.35023998408983]
The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review.<n>We propose ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines.<n>This paper establishes essential metrics for AIbased peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.
arXiv Detail & Related papers (2025-02-17T12:22:11Z) - Development of Application-Specific Large Language Models to Facilitate Research Ethics Review [0.0]
We propose application-specific large language models (LLMs) to facilitate IRB review processes.<n>These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets.<n>We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support.
arXiv Detail & Related papers (2025-01-18T12:05:05Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [55.33653554387953]
Pattern Analysis and Machine Intelligence (PAMI) has led to numerous literature reviews aimed at collecting and fragmented information.<n>This paper presents a thorough analysis of these literature reviews within the PAMI field.<n>We try to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews; (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews; and (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - A Critical Examination of the Ethics of AI-Mediated Peer Review [0.0]
Recent advancements in artificial intelligence (AI) systems offer promise and peril for scholarly peer review.
Human peer review systems are also fraught with related problems, such as biases, abuses, and a lack of transparency.
The legitimacy of AI-driven peer review hinges on the alignment with the scientific ethos.
arXiv Detail & Related papers (2023-09-02T18:14:10Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Automated scholarly paper review: Concepts, technologies, and challenges [5.431798850623952]
Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process.
With the involvement of humans, such limitations remain inevitable.
arXiv Detail & Related papers (2021-11-15T04:44:57Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.