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

Researchers propose AI as a Research Object (AI-RO), a governance framework that treats generative AI interactions as inspectable, documented components of scientific research rather than debating authorship. The framework combines interaction logs, metadata packaging, and provenance records to ensure accountability, particularly for security and privacy research where confidentiality and auditability are critical.

The paper addresses a fundamental tension in modern science: how to integrate generative AI into research workflows while maintaining scientific integrity and reproducibility. Rather than settling philosophical debates about AI authorship, the authors propose a pragmatic governance model that emphasizes transparency through structured documentation. This shift from authorship to accountability represents a maturation in how the scientific community approaches AI tooling.

The AI-RO framework builds on established Research Object theory and FAIR (Findable, Accessible, Interoperable, Reusable) principles, creating a standardized way to capture model configurations, prompts, outputs, and interaction logs. This approach gains particular importance in sensitive domains like security and privacy research, where traditional disclosure practices inadequately address confidentiality, integrity, and auditability requirements. The authors demonstrate feasibility through a lightweight implementation where language models synthesize literature review notes while maintaining verifiable provenance records.

For the broader scientific ecosystem, this framework could significantly reduce friction around AI use in research. Institutions and funding bodies have struggled with policy responses to generative AI; a standardized documentation approach provides concrete guidance without requiring outright bans. The work also has implications for AI governance more broadly, suggesting that trustworthiness derives from systematic transparency rather than trust in the system itself.

The framework's adoption will depend on practical implementation tools and community adoption. Future developments must address scalability, standardization across disciplines, and integration with existing research infrastructure. Success could establish a precedent for governance models that balance innovation with accountability across sectors beyond academia.

• →AI-RO paradigm shifts focus from authorship debates to systematic documentation and accountability in AI-assisted research
• →Framework leverages Research Objects and FAIR principles to create verifiable provenance records with integrity guarantees
• →Structured disclosure approach addresses unique confidentiality and auditability needs of security and privacy research
• →Lightweight implementation demonstrates feasibility of integrating generative AI with controlled, inspectable workflows
• →Adoption could provide institutional guidance for AI governance without requiring restrictive policies