Introduction
In the transition into digital spaces and the data economy, use of quality data becomes important for good governance and policy development. Developments in AI have also contributed to the increased reliance on large datasets. Around the world, new tools are being explored and tested to provide collective and participatory means of governing data. The idea is to promote the management of data in ways that benefit those from whom data is collected. Data trusts are one such tool that have received much attention globally. While there are many iterations of what a data trust is and how it can function, the key premise is that it is a legally enforced governance structure wherein members pool their personal data and entrust a data trustee or trustees, who have a fiduciary responsibility to act in the best interests of the members of the trust, to determine how this data is to be used and managed.
Three aspects are key to data governance: collection (who collects the data?), management and access (who has access to the data and why?) and use (who decides how the data is used, who is using the data and why?).[1] The answer to the ‘who’ of it all in each aspect determines the purposes to which data may be put, the limitations on its use, and the role of data subjects and their consent in shaping this. However, the ‘who’ of it all leads to other questions (i.e. how, when, why, where and what) that shape data flows. In the past, those who collected, held or used data generally claimed the right to make decisions about what happened with the data, often subject to minimal legal restrictions for data subjects around consent or appeal at certain points of data flow. This has led to significant concerns about the exploitation of data, and the exploitation of individuals and communities through data, as well as concerns that data is not being adequately used to address questions of public interest and benefit. As new mechanisms are developed to strengthen the responsible governance of data in an increasingly datafied society, we must critically assess the applicability of tools such as data trusts in different parts of the world with different legal structures, social contexts, and data needs, in order to ensure that these tools improve data justice-related outcomes for people and communities.
This paper discusses whether data trusts are feasible structures in an African context, concluding that there are significant limitations to a straight import of trust models developed elsewhere. It goes on to outline specific considerations that should be prioritised in the development of bottom-up and collective models of data governance on the continent, whether adopting a formal trust structure or not. This is done through a brief overview of data trusts and looking at data rights in Africa with particular focus on South Africa’s data protection law. The paper then delves into the contentions of a Global South and Global North approach by highlighting the limitations of data trusts in an African context. It argues that the development of data trusts could still offer critical benefits especially when informed by African values and historical contexts. Making use of international instruments (Banjul Charter), principles (CARE) and values (Ubuntu), the paper emphasises the importance of collective decision-making relating to data and concludes with recommendations on collective and participatory governance, women’s empowerment and capacity-building, to highlight how the alignment of data trusts to African contexts could help balance historical power differentials in the digital age.
