Open Licensing and Data Trust for Personal and Non-Personal Data: A Blueprint to Support the Commons and Privacy

Abstract

The present contribution proposes a novel commons-based copyright licensing model that provides individuals better control over all their data (including copyrightable, personal and technical data) and that covers recent developments in AI technology. The licensing model also proposes restrictions and boundaries (e.g. to authorised users and groups) to protect the commons, allowing communities to define and maintain the political values they choose. Building on the practice of collective management of copyright, it also empowers data trusts to govern and monitor the use and re-use of the concerned data. The model is ultimately meant to address the power imbalance and information asymmetry that characterise today’s economy of data as well as the “data winter” effect that restricts the accessibility of data for public interest, while accommodating and empowering individuals and communities that may have different political values and visions.

Keywords: open licenses, open data, data commons, data trusts, data governance, data trust, licensing personal data