Participated in a couple events on this topic in late January

• One hosted by ODI, Global Partnership for AI and Aapti exploring whether a data trust is a feasible model for farmers to pool data and get access to advisories that promote climate resilience. More on their project here

• What ODI/GPAI/Aapti are looking for are use cases where there is potential community demand for a data trust; they are keen to support such an initiative in prototyping the idea. 

• Short answer, accessing advisories is not a compelling enough hook to get farmers to pool data. If data is going to be collected, makes sense to leverage it for multiple purposes like certifications and access to credit.

• One of the attendees was Nipun from The Agri Collaboratory (TAC, more here) who reiterated this point. He is exploring whether a trust makes sense for a farmer credit scoring model. I think we may have come across them in our FS work, rikin (Unlicensed) and Vineet Singh can add more

• An interesting thread on who is the data trustee. It can be a civil society organisation, or a group of members of a cooperative. The Non Personal Data Report in India says that non-profits or even govt organisations can serve as trustees, but in an ideal scenario, it has to be a group nominated by the members of the trust to serve as trustees. Important to note that there are legal burdens placed on the trustee so they need a real incentive to play this role

• One positive aspect of trusts, esp in India is that there is a strong legal underpinning; over a hundred years of trust law and jurisprudence that could be useful. 

• MIRO board from the session here.

• Birchip Cropping Group from Australia was called out as an interesting model; farmers 

• This is a nice post on the idea of “bottoms up data institutions” which is relevant to our DFN approach

• I could see this group being a useful resource to bounce ideas as we develop DFN and I believe we have engaged with ODI previously as part of FS as well. Shreya Agarwal (Unlicensed)

• Mozilla Foundation held an event which laid out design principles for Data Trusts which build upon commons governance principles developed by Elinor Ostrom (see here).

  • Elinor Ostrom’s work is awesome, btw, this is a nice summary post and quote that shows how her thinking is really aligned with DG’s values

Humans have a more complex motivational structure and more capability to solve social dilemmas than posited in earlier rational-choice theory. Designing institutions to force (or nudge) entirely self-interested individuals to achieve better outcomes has been the major goal posited by policy analysts for governments to accomplish for much of the past half century. Extensive empirical research leads me to argue that instead, a core goal of public policy should be to facilitate the development of institutions that bring out the best in humans.

• Full meeting notes here

• One link that was shared during that session is this framework for thinking about what sort of structure makes sense for data sharing including a data trust vs. data cooperative vs. data union.

Data Coops: the contractual underpinnings of data coops make them relatively easy to set up and well suited to empowering groups of individuals to obtain individual goods (such as financial returns) or services (such as the monitoring of health or education services) that they would not be able to secure without pooling their data. Data coops may to some extent facilitate the pursuit of societal goods (such as health research) on an ad-hoc basis (depending on the contractual terms).

Public Databank: (à la Sidewalk labs latest instantiation): The fact that this type of data institution is run or held by a public entity makes it particularly well suited to purposes relating to the furthering of societal goods. Nothing prevents this type of institution from also endeavouring to facilitate the delivery of individual goods (like monitoring the quality of health services). This type of institution may seek to address or minimize the vulnerabilities that stem from data sharing. Its being a State-provided, monolithic type of institution may however hinder its ability to address some types of vulnerabilities and enfranchise marginalised groups.

Data Trusts: Data Trusts distinguish themselves from data coops and public databanks not only through the level of legal safeguards they are able to provide (within the framework of trust law). They are also uniquely capable of any combination of these aims (depending on the focus of each particular data trust). This mechanism is well-placed to address concerns about enfranchisement, by providing a mechanism for under-represented and potentially vulnerable groups of individuals to reverse the direction of consent: their data trustee will have the fiduciary responsibility to exercise their data rights in a way that promotes the aspirations set out in the terms of each trust.