AI Training Data Traceability and Automated IP Remuneration

Artificial intelligence is transforming how digital content is created, shared and reused. Foundation models and generative AI increasingly rely on vast amounts of training data originating from a diverse range of creators, organisations and digital platforms, as well as distributed data silos. While these developments create significant opportunities for innovation, they also raise fundamental challenges regarding copyright, transparency, licensing and the fair distribution of economic value.

Existing IP licensing systems were largely designed for comparatively simple licensing relationships and are increasingly challenged by the scale, complexity and decentralised nature of modern AI ecosystems. In many cases, it remains difficult to determine which content contributed to model development, under which licensing conditions it was used, and how rights holders should be fairly compensated.

This research investigates how digital licensing ecosystems can enable transparent, scalable and trustworthy intellectual property management for AI training data. The project develops an integrated framework that combines data provenance, attribution, machine-readable licensing and automated remuneration into a coherent digital licensing infrastructure. It explores how distributed ledger technologies [e.g., smart contracts, tokenised licensing mechanisms, Automated Market Makers (AMMs), decentralised governance such as DAOs] can support trustworthy licensing processes and at the same time reduce transaction costs and improve transparency across AI value chains.

Beyond the underlying technological architecture, the research examines strategic questions at the intersection of intellectual property management, innovation management and digital platform ecosystems. It investigates how licensing ecosystems can create and capture value while aligning the incentives of creators, data providers, platform operators as well as AI developers. The research further seeks to contribute to the design of scalable, transparent and economically sustainable licensing mechanisms. It also aims to support responsible AI innovation and strengthen trust in the emerging digital economy. In doing so, the research contributes to advancing several United Nations Sustainable Development Goals (SDGs), particularly those related to innovation, sustainable industrial development and responsible digital transformation.

Project lead: Jan-Gero Alexander Hannemann
Project team members: Prof Frank Tietze