Data trusts: a practical way forward for schools and data?

One possible change for schools looking to make better use of their data is to establish some form of ‘data trust’. This idea is outlined in some detail (alongside other alternate data governance mechanisms) in a joint publication from the Ada Lovelace Foundation and AI Council, titled ‘Exploring legal mechanisms for data stewardship’.

‘Data Trusts’ are based around the long-standing legal tool of ‘Trust Law’ – historically used in English Common Law for managing the estates of overseas medieval soldiers, deceased people and other forms of shared property. In essence, a ‘trust’ is a legal relationship between one party (the trustee) which manages the rights associated with an asset for the benefit of other parties (the beneficiaries). This management arrangement is set up to ensure that different parties’ access to and rights to the asset are determined in an equitable manner – i.e. according to principles of fairness and justice.

Centuries on from the first uses of Trust Law, digital data is beginning to be seen as an appropriate asset to be governed by trust arrangements. The Lovelace document makes the point that present uses of digital data tend to be beset by inherent structural power imbalances between individuals and ‘big tech’ actors. In short, the value of data lies in the aggregation of many individuals’ data into big data sets – something that is most readily done by platform providers or data companies as part of what individuals ‘consent’ to do when agreeing to the terms of service.

As noted throughout the critical data studies literature, this imbalance in scale leaves individuals in a vulnerable position – unable to fully understand what is being ‘consented’ to, and open to a range of discriminations, harms and other potential misuses of their data. In an institution such as a school, one’s leeway to ‘opt out’ of using a data-driven system is often impractical.

Applying trust law to the governance of digital data therefore increases each individual’s capacity to exercise the data rights that they currently have in law, while allowing groups of individual to collectively define the terms under which their data should be used. As such, establishing a data trust as a means of data governance with a school is a way of balancing the obvious asymmetries of power between school authorities (in their powerful role as data controllers) and individual students, parents and even teachers (in their less powerful roles as data subjects).

A key element of any data trust is the role of the ‘trustee’ – tasked with acting in good faith in ways that leverage the interests of the beneficiaries. Trustees can negotiate data-sharing agreements with other actors (such as for-profit data brokers, or non-profit research institutes) who want to make use of the pooled data represented in the trust.  The trustee role demands a high level of data and legal knowledge, along with a fiduciary duty to always act in the best interests of the trust’s beneficiaries. This is a serious undertaking, with trustees legally held to account by the constitutional terms of the trust. The success of any data trust depends on the appointment of an appropriate trustee.

The first chapter in ‘Exploring legal mechanisms for data stewardship’ actually concludes with a hypothetical description of how a high school might establish a data trust as a form of data governance. Here is it imagined that several schools might club together to form a data trust to pool the data generated from a commonly-used learning management system (this might include test scores, usage data, other indications of learning progress). This group of schools is represented by a board – with one of the board members appointed as the data trustee. Through these mechanisms the data trust can then work to … 

  • keep students and parents informed about the storage and use of their data; 
  • share anonymised performance data to allow school leaders to make meaningful comparisons across and within each of their schools; 
  • negotiate the terms on which data might be sold back to the learning platform company to help improve its products and services.
  • Decide on which basis the pooled data might be shared with other non-profit actors – e.g. government department of education, non-profit researchers, teaching unions and professional associations
  • Decide on which basis the pooled data might be sold to appropriate for-profit actors – e.g. commercial data brokers.