A Digital Content Provenance Approach to Support Attribution, Copyright, Intellectual Property Claims, and Governance for Human-AI Created Works

Abstract

Digital content is increasingly produced through workflows involving generative artificial intelligence, raising complex questions of authorship, copyright, disclosure, licensing, responsibility, and governance. These questions can only be answered meaningfully when reliable information exists about the people, tools, resources, and processes involved in the creation of a work. Yet a persistent informational gap remains. This research addresses that gap by designing and evaluating a governance-oriented provenance approach for human-AI-created works.

The study adopts a Design Science Research Methodology and combines normative doctrinal analysis with structured artefact evaluation. Legal and regulatory analysis of the European Union, the United States, and the World Intellectual Property Organization framework is used to derive governance requirements, while existing provenance and traceability systems are reviewed to establish the technical landscape and inform the design. On that basis, the research develops a workflow-level provenance approach, including a new Entity-Action-Attribution data model and an extensible metadata structure for representing governance-relevant information such as AI involvement, rights reservations, and authorisation conditions. The approach is implemented and demonstrated in representative application scenarios, including a multimodal chatbot-based environment, and evaluated against the requirements derived from the legal and technical analysis.