Co-designing data trusts for climate action
Vinay Narayan, Aapti Institute & Joe Massey, ODI
Environmental data is used in many different ways to combat climate change, from improving the energy efficiency of our homes to helping to combat deforestation. It has become central to both top-down and bottom-up environmental movements, and in the face of a global climate crisis, these movements have never been as urgent as they are now.
However, as with data systems at large, individuals and communities tend to have little say in how data is collected, used and shared for climate action. Data trusts and other forms of ‘bottom-up’ data stewardship have emerged to reverse this trend and empower people to take part in the data economy, and to further contribute to tackling the climate crisis.
Aapti Institute and the Open Data Institute, in partnership with the Global Partnership on Artificial Intelligence (GPAI), undertook a research study to co-design and evaluate the feasibility of bottom-up data trusts in tackling the climate crisis and its impacts.
Why is data stewardship important?
Significant advancements in the field of artificial intelligence have bolstered the fight against climate change by distilling raw data into actionable information and accelerating scientific modelling.
In order to train, develop and deploy these AI models we need access to data, and given the complexity of the challenge we face in the climate crisis, we need many different types of data from multiple sources. Data stewardship is an approach to data governance that aims to unlock the societal value of data in a ‘rights preserving way’, and can be viewed in response to the common scenarios of ‘data hoarding’, where data is locked away in silos by a few organisations, and ‘data fearing’, where data is not shared for fear of privacy and related harms arising from data misuse.
The idea of ‘bottom-up data stewardship’ is a tonic to the typical exclusion of individuals and communities from data collection, use and sharing. Bottom-up data stewardship recognises individuals and communities as more than mere providers of consent (or recipients of information about how data about them is used), and seeks to empower them to participate in the process of data collection, use and sharing. It provides them with a say in decisions over who has access to data about them, how that data is shared, for what purposes and to whose benefit.
Bottom-up approaches to data stewardship are already being used to tackle the climate crisis. They are often framed as ‘citizen science’, whereby individuals contribute data towards a particular project through observations or sensors, such as iNaturalist and Sensor.Community.
Why data trusts?
Articulated as a collective action tool for communities that enables them to govern how their data is shared with and used, data trusts have seen various definitions, each highlighting different criteria. The Data Governance Working Group of GPAI reconciles these differences by identifying 5 functions that are core to a data trust:
mutually agreed terms and conditions of data use
expert trustees who govern the trust’s assets;
strong fiduciary responsibilities binding trustees to act in the interests of the trust’s members;
use of trust assets in accordance with agreed terms and conditions; and
safeguards and oversight mechanisms to prevent data misuse and to take remedial action.
Not all of these functions are unique to data trusts. For instance, data cooperatives and data collaboratives also allow for the negotiation of data in accordance with agreed terms and provide safeguards to prevent data misuse. The key component of a data trust that differentiates it from other forms of data stewardship is the placing of fiduciary duties on independent trustees. Fiduciary duties imply that the trustees have a higher than normal, legally bound, a duty of care towards the beneficiaries of the trust, making data trusts inherently more trustworthy than other forms of stewardship. This is especially important given the potential for misuse of data, and the threats to privacy and other rights that such misuse can entail.
Our feasibility study of data trusts in climate
As part of our study, we co-designed three bottom-up data trusts that can impact domains contributing to and /or affected by climate change: (a) a data trust for cyclists in London (page 14 of the report); (b) a data trust for small shareholder farmers in India (page 26); and (c) a data trust related to indigenous climate displacement in Peru (page 37).
We evaluated the feasibility of these based on a set of 8 criteria: the incentive to create a data trust, capacity to run and engage with a data trust, the existence of necessary legal levers to create and run a data trust, the technological feasibility of a data trust and financial sustainability of the data trust. Based on our analysis, we determined the London Cycle Data Trust to be the most feasible, while the feasibility of the other two data trusts was less certain.
The feasibility of the London Cycle Data Trust was a result of a few key factors: the clear demand for the improvement of cycling infrastructure in London, a high demand for mobility data from local authorities for more informed decision making, a high level of digital literacy amongst potential users of the data trust, the availability of multiple legal forms for the data trust to take and strong digital infrastructure in London.
However, many of these factors were missing with the other two data trusts. Not only do farmers in India not possess the required level of digital literacy to engage with a data trust, but the lack of adequate digital infrastructure in India (including internet access) in most parts of rural India is also a major limiting factor. This is compounded by the continuing lack of data protection legislation that recognises key rights over data and provides guardrails against the misuse of data.
In Peru, while there is a strong demand for accurate data regarding climate displacement, the relatively low levels of digital literacy amongst the communities that are at risk of displacement was a major hindering factor.
Key Takeaways
We’re optimistic about the potential for a London Cycle Data Trust and other, similar data trusts to emerge to enable communities to use data to advocate for, and inform the design of, sustainable transport infrastructure. Existing initiatives such as Posmo.Coop and the Bike Data Project show that there is interest and motivation among communities and that there are defined uses for data in this context, as well as momentum behind the idea. While the feasibility of data trusts for small shareholder farming in India or for indigenous climate migration in Peru is much less certain, there are clearly opportunities to improve how data is collected, used and shared in these areas and we hope this work serves to highlight them.
While our work demonstrated the high potential for a data trust in a specific context, it also highlighted the high barriers for success facing data trusts in other areas. While the differentiating characteristics of a data trust are theoretically attractive ones (the placing of fiduciary duties on the trustees), in a practical setting these characteristics have yet to be applied and tested.
Thankfully, data trusts are but one manifestation of responsible data stewardship, proposed as a tonic to problematic ‘one size fits all’ approach to data governance. In some cases, other forms of bottom-up data stewardship may be better suited to meeting the needs of their beneficiaries. We can see some of these in action already, empowering individuals to take more control over their data. Driver’s Seat Cooperative is one such example. A driver-owned data cooperative, Driver’s Seat allows drivers in the gig economy to maximise their rideshare and delivery earnings through use of their data.
Furthermore, given that bottom-up institutions presuppose a degree of digital literacy and capacity to engage on data rights, there is a need to explore a range of different institutions. In such contexts (which are more common in the Global South) there is merit in encouraging stewards that are not set up by the members themselves but by an organisation (typically an NGO or CSO) that nonetheless acts in the interests of the communities, they work with. Abalobi, a South African non-profit that empowers local fishers through the use of their data, is a great example of this. While not set up by the fishers themselves, Abalobi collects and stores data on behalf of the fishers and shares data with third parties only if the fishers consent to it. They also help fishers visualise the data they collect and use it to their benefit. Alternatively, existing institutions such as cooperatives and unions can also be empowered to transition to becoming stewards of the data relating to their members. Given the scale of their membership base, their trust within the community and financial sustainability, pivoting existing community organisations to data stewards can be very promising. PescaData, an initiative of COBI, is another example of this.
Our work set out to test the feasibility of data trusts for climate action and identify the potential contexts where they could work. Given certain base conditions, data trusts serve as a wonderful tool for participatory data governance that provides users with agency over their data whilst making data available for public good, in a democratic, rights-respecting manner. The type of support and development by the Data Trusts Initiative is crucial in bringing data trusts to life, and is the first step in understanding which contexts data trusts can affect change, and where other approaches to data stewardship may have more success in combating the climate crisis.