AI, open science, and the extraction imperative: A situated approach to the normativities of open research data

Abstract

Open Science has long been framed as a normative project grounded in values of transparency, accessibility and collaboration. Yet in the age of generative AI, many research data practitioners, who once championed openness, are increasingly questioning how these commitments can be upheld when openness also enables undesired large-scale data extraction, decontextualization and enclosure. This paper examines how the normativities of openness are being re-negotiated, and how researchers navigate the ethical and political tensions that arise when their work is repurposed for proprietary AI systems. Drawing on in-depth qualitative interviews, I explore how situated ethical reasoning informs decisions about data sharing. Participants described new risks and dilemmas, from uncontrolled scraping and heightened re-identification threats to the reframing of FAIR (Findable, Accessible, Interoperable, Reusable) principles as pipelines for AI-readiness. Their accounts reveal openness as a relational and contested practice, embedded in specific institutional, infrastructural and community contexts, and continuously re-balanced between care, accountability and collaboration. Rather than rejecting openness outright, interviewees described recalibrating it – through selective sharing, alternative infrastructures and advocacy for commons-based governance models. Framed by critical data studies, and science and technology studies, the analysis conceptualizes these practices as reflexive agency: the situated capacity to align openness with evolving ethical and political conditions. The findings argue for re-politicizing openness as a matter of commons stewardship and situated ethics, aligning governance, infrastructure and normative commitments to resist extractive appropriation while sustaining the collective conditions for shared knowledge production.