• Chih-hsing Ho

Governance of Communal Data Sharing

This blogpost presents an overview of the chapter in Good Data (edited by Angela Daly, S Kate Devitt and Monique Mann) on communal data sharing which Prof Chih-hsing Ho (Law) co-authored with Prof Tyng-Ruey Chuang (Computer Science), both of whom are based at Academia Sinica, Taiwan. Prof Ho was a guest of Prof Angela Daly and CUHK Law thanks to the CUHK University Academic Exchange Fund and participated in two launch events, including one organised by CFRED, for the Good Data book in Hong Kong in March 2019.

Today, digital wealth is being monopolized and concentrated in very few hands. This dominance has led to such side effects as unfair competition, manipulation, routine intrusion of privacy, and the undermining of democracy. Tech giants provide the infrastructure undergirding much of the data economy, and stand to gain the most from it. Although most of their services appear to be free, what underlies the transactions of the digital economy is an exchange of services for control over data. The challenges posed by capitalist accumulation of data raise the question: is this monopoly inevitable? How are we to imagine and create different systems, fairer systems featuring greater participatory control?

Our chapter in Good Data proposes theoretical and computational approaches to the analysis of an alternative data sharing model, which is based on community participation in decision making and self-governance. When we talk about “community,” we use this term in a non-conventional way. We try not to see community as a fixed group or a predefined collective identity. Rather, it refers to a set of ongoing engagement and practices of group making. In other words, it is this dynamic process of community making — acts of mutual support, negotiation and experimentation, as David Bollier has argued — that are needed to build innovative systems to manage shared resources. Along with these curiosities, we consider several examples, such as user data cooperatives and collaborative data projects, to further explore how a community is formed and how the governance of communal data sharing is being established. We will then develop frameworks for the governance of communal data sharing by combining common pool resource management and a socio-legal perspective on the commons.

That said, the social practice of commoning is a political-economic alternative to standard capitalist practice. For commoners, what is more important is the fair conditions under which surplus is produced, and that the decision making about the surplus to be distributed involves those who take part in the process of production. Applying the idea of the commons to the data economy, this participatory form of data sharing addresses the well-being of others through a process of democratizing ownership. Commoners need to communicate with one another to develop the norms, protocols or rules that govern access and the management of shared resources they co-own. In this process of commoning, all parties are stakeholders and are equally affected and bound by the governing rules they discuss, negotiate and then agree upon. It is such ongoing social relationships that help build distinct communities in which commoners form their own subjectivities.

Current legal and regulatory frameworks for data protection fail to address the devastating problem of market enclosure. By focusing on consent and the anonymisation of data, these legal techniques echo the neo-liberal methods of governance which promise individual autonomy and choice as an advanced liberal strategy. A communal approach to data sharing aims to create a decentralized model under which data subjects and data controllers are united rather than separated. In other words, norms and principles for data use can be decided upon data subjects who are members of the community. Several notable experiments illustrate this kind of peer-based information production and sharing. Wikipedia, OpenStreetMap, and Social.Coop are examples. They demonstrate that data, information and knowledge can be aggregated, shared and managed by the peers themselves for the maximum of communal benefits. In addition, these initiatives also show that data management can be achieved from the bottom-up through grass root efforts.

When enormous opportunities emerge along with making use of vast amounts of data, challenges are generated and concerns arise around monopoly and market enclosure. We need to ensure that the rapidly developing data economy evolves in fair and just ways. In order to make possible this goal, it is crucial that an innovative, bottom-up and de-centralized data governance framework be designed, through which a trustful space arises such that all stakeholders are able to fruitfully engage and take responsibility for their communities. A communal data sharing model is established based on these principles. We therefore propose this data sharing model to help create fairer platforms for everyone who takes part in this brave new data-driven revolution.

Chih-hsing Ho, Academia Sinica

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