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

2026-04-13

AI Summary

This study addresses the lack of effective governance mechanisms for generative AI in scientific research, which undermines auditability, traceability, and regulatory compliance. To remedy this, the paper introduces a novel paradigm—“AI as a Research Object” (AI-RO)—that treats AI interactions as structured components within the research workflow. Grounded in research object theory and FAIR principles, the approach establishes a verifiable provenance mechanism by systematically capturing model configurations, prompts, and outputs. The authors implement a lightweight writing pipeline that integrates interaction logs, metadata encapsulation, and constraint-driven language models to automatically generate structured literature reviews accompanied by comprehensive provenance records. This framework demonstrates how generative AI can be employed in research with enhanced trustworthiness and transparency.

Technology Category

• Natural Language Processing: Generation
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

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.

Problem

• generative AI governance
• Research Object
• provenance
• FAIR principles
• security and privacy

Innovation

• Research Object
• Generative AI Governance
• Provenance Capture
• FAIR Principles
• Inspectable AI