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

Catellacci, Fulvio, Ciarli, Tomasso, Gao, Yuan ORCID: https://orcid.org/0000-0003-3505-9304, Marino, Marianna, Marzi, Giacomo, Riccaboni, Massimo, Savona, Maria and Vannuccini, Simone (2026) Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI.

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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.

Item Type:	Article
Uncontrolled Keywords:	generative artificial intelligence,research governance,responsible research and innovation,disclosure,adverse selection,knowledge commons,sdg 16 - peace, justice and strong institutions ,/dk/atira/pure/sustainabledevelopmentgoals/peace_justice_and_strong_institutions
Faculty \ School:
UEA Research Groups:	Faculty of Social Sciences > Research Centres > Centre for Competition Policy Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance Faculty of Social Sciences > Research Groups > Industrial Economics
Related URLs:	http://arxiv.org/abs/2607.24879
Depositing User:	LivePure Connector
Date Deposited:	13 Aug 2026 12:12
Last Modified:	13 Aug 2026 12:12
URI:	https://ueaeprints.uea.ac.uk/id/eprint/104116
DOI:	10.48550/arXiv.2607.24879

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