Inspectable AI for Science: A Research Object Approach to Generative AI Governance
For the scientific community, this work offers a governance paradigm for generative AI that emphasizes documentation and provenance, though it is presented as a position with a demonstrative workflow rather than a validated solution.
This paper proposes treating generative AI interactions as structured, inspectable research objects (AI-RO) to govern AI use in science, implementing a lightweight pipeline that produces verifiable provenance records. The approach aims to address accountability and auditability challenges, particularly in security and privacy research.
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.