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arXiv · 2604.11261

Inspectable AI for Science: A Research Object Approach to Generative AI Governance

Ruta Binkyte·Sharif Abuaddba·Chamikara Mahawaga·Ming Ding·Natasha Fernandes·Mario Fritz
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Abstract

This paper introduces AI as a Research Object (AI-RO), a paradigm for governing the use of generative AI in scientific research. Instead of debating whether AI is an author or merely a tool, we propose treating AI interactions as structured, inspectable components of the research process. Under this view, the legitimacy of an AI-assisted scientific paper depends on how model use is integrated into the workflow, documented, and made accountable. Drawing on Research Object theory and FAIR principles, we propose a framework for recording model configuration, prompts, and outputs through interaction logs and metadata packaging. These properties are particularly consequential in security and privacy (S&P) research, where provenance artifacts must satisfy confidentiality constraints, integrity guarantees, and auditability requirements that generic disclosure practices do not address. We implement a lightweight writing pipeline in which a language model synthesizes human-authored structured literature review notes under explicit constraints and produces a verifiable provenance record. We present this work as a position supported by an initial demonstrative workflow, arguing that governance of generative AI in science can be implemented as structured documentation, controlled disclosure, and integrity-preserving provenance capture. Based on this example, we outline and motivate a set of necessary future developments required to make such practices practical and widely adoptable.

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Ruta Binkyte, Sharif Abuaddba, Chamikara Mahawaga, Ming Ding, Natasha Fernandes, Mario Fritz. 2026-04-13. Inspectable AI for Science: A Research Object Approach to Generative AI Governance. https://arxiv.org/abs/2604.11261

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