Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI

2026-07-27

AI Summary

This study addresses the systemic risks posed by generative artificial intelligence in scientific research—namely, knowledge-base negative externalities, erosion of research capabilities, and information asymmetry—even as it enhances research efficiency. To mitigate these challenges, the work proposes a “Responsible Research with AI” (RRAI) framework grounded in four principles: disclosure, differentiation, narratability, and proportionality. It systematically examines AI’s impacts across four research phases: funding allocation, conduct, peer review, and application. Drawing on academic roundtable discussions, empirical literature reviews, and institutional analysis, the study develops a multilayered governance pathway aligned with existing mechanisms such as the EU AI Act. Findings indicate that while AI boosts publication output and citations, its contribution to genuine innovation remains limited. The RRAI framework offers a viable approach to balancing individual researcher benefits with the collective health of the scientific ecosystem.

Abstract

This paper examines the tension between the benefits of generative artificial intelligence (AI) for scientific research and the unresolved governance questions that accompany its rapid adoption. Drawing on an academic roundtable held at the AI for Science and Innovation Workshop (Scuola IMT Alti Studi Lucca, April 2026) and on a fast-expanding empirical literature, it maps the disagreement within the research community across four stages of the research process: funding, research tasks, publication and peer review, and use and uptake. The empirical case for AI's productivity, augmentation, and democratization effects has strengthened. The picture changes once productivity is disaggregated: AI-assisted work shows measurable gains in publication volume and citation share, while the evidence on novelty, disruption, and breakthrough output remains ambiguous or negative. We argue that the divergence between private and social returns arises through three analytically distinct mechanisms, namely information asymmetry, negative externalities on a shared knowledge base, and depletion of research capacity, and that each calls for a different governance instrument. We propose Responsible Research with AI (RRAI), an extension of the Responsible Research and Innovation tradition organized around four principles that operate at different levels of the research system: disclosure, differentiation, narrative, and proportionality. RRAI builds on existing institutional scaffolding, including the EU AI Act, UNESCO, and the OECD, and aims to preserve AI's productivity gains while addressing systemic risks that individual researchers can neither observe nor manage on their own.

Problem

• Generative AI
• Scientific Research
• Governance
• Collective Risks
• Responsible Innovation

Innovation

• Responsible Research with AI
• generative AI
• research governance
• negative externalities
• scientific productivity