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

TL;DR

This paper proposes AI as a Research Object framework to govern generative AI in science through structured, inspectable documentation and provenance, enhancing accountability and security in research workflows.

Contribution

It introduces a novel AI-RO paradigm for scientific governance, integrating model use into research workflows with structured logs and metadata for transparency and accountability.

Findings

Implemented a prototype pipeline for documenting AI model interactions.

Demonstrated how provenance and security constraints can be integrated.

Outlined future steps for practical adoption of AI-RO in science.

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.