Enabling Data Sharing for Social Benefit Through Data Trusts

The Global Partnership on AI
, 2022.

Abstract

According to the World Meteorological Organization, 2019 concluded a decade of exceptional global heat, retreating ice and record sea levels driven by greenhouse gases produced by human activities.

Data and artificial intelligence have a vital role to play in helping us understand and tackle this climate crisis, from predicting extreme weather events, to improving the energy efficiency of our homes and helping to identify deforestation.

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.

This report sets out the work undertaken for the project ‘Data trusts in climate’, completed between November 2021 and March 2022. The project was commissioned by the Global Partnership on Artificial Intelligence (GPAI) and delivered in partnership by the Open Data Institute (ODI) and Aapti Institute, with support from the Data Trusts Initiative. It consisted of a literature review, expert interviews and a co-design process involving more than 50 organisations from around the world.

We articulate a design for a London Cycling Data Trust, describing how it could interact with the cycling community, be legally incorporated, the technologies it could use and its options for funding. We found that data trusts will be less feasible in other contexts and difficult to apply for a variety of reasons, including ones of cultural, technological and economic nature. We set out lessons learned from exploring data trusts for small shareholder farming in India and for indigenous climate migration in Peru.

This work has produced a set of generic feasibility criteria for data trusts, which are intended for use by policymakers and practitioners seeking to understand where data trusts may be necessary and possible, in climate change and beyond. It also sets out a practical roadmap for the development of a London Cycling Data Trust and other similar data trusts, outlining the steps required to implement it in the real world.

We also discuss other approaches to responsible data stewardship for AI, and options for GPAI and other policymakers interested in this agenda.

As in all progress around new approaches to data stewardship, it is essential to test these ideas further. This project is another step forward for data trusts, but much more is required to unlock their potential.

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BibTeX
@Misc{publications/enabling-data-sharing-for-social-benefit-through-data-trusts, title = {Enabling Data Sharing for Social Benefit Through Data Trusts}, author = {{The Global Partnership on AI}}, year = {2022}, pdf = {https://gpai.ai/projects/data-governance/data-trusts-in-climate-interim-report.pdf}, url = {/publications/enabling-data-sharing-for-social-benefit-through-data-trusts}, abstract = {According to the World Meteorological Organization, 2019 concluded a decade of exceptional global heat, retreating ice and record sea levels driven by greenhouse gases produced by human activities. Data and artificial intelligence have a vital role to play in helping us understand and tackle this climate crisis, from predicting extreme weather events, to improving the energy efficiency of our homes and helping to identify deforestation. 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. This report sets out the work undertaken for the project ‘Data trusts in climate’, completed between November 2021 and March 2022. The project was commissioned by the Global Partnership on Artificial Intelligence (GPAI) and delivered in partnership by the Open Data Institute (ODI) and Aapti Institute, with support from the Data Trusts Initiative. It consisted of a literature review, expert interviews and a co-design process involving more than 50 organisations from around the world. We articulate a design for a London Cycling Data Trust, describing how it could interact with the cycling community, be legally incorporated, the technologies it could use and its options for funding. We found that data trusts will be less feasible in other contexts and difficult to apply for a variety of reasons, including ones of cultural, technological and economic nature. We set out lessons learned from exploring data trusts for small shareholder farming in India and for indigenous climate migration in Peru. This work has produced a set of generic feasibility criteria for data trusts, which are intended for use by policymakers and practitioners seeking to understand where data trusts may be necessary and possible, in climate change and beyond. It also sets out a practical roadmap for the development of a London Cycling Data Trust and other similar data trusts, outlining the steps required to implement it in the real world. We also discuss other approaches to responsible data stewardship for AI, and options for GPAI and other policymakers interested in this agenda. As in all progress around new approaches to data stewardship, it is essential to test these ideas further. This project is another step forward for data trusts, but much more is required to unlock their potential. } }
Endnote
%0 Generic %T Enabling Data Sharing for Social Benefit Through Data Trusts %A The Global Partnership on AI %D 2022 %F publications/enabling-data-sharing-for-social-benefit-through-data-trusts %U /publications/enabling-data-sharing-for-social-benefit-through-data-trusts %X According to the World Meteorological Organization, 2019 concluded a decade of exceptional global heat, retreating ice and record sea levels driven by greenhouse gases produced by human activities. Data and artificial intelligence have a vital role to play in helping us understand and tackle this climate crisis, from predicting extreme weather events, to improving the energy efficiency of our homes and helping to identify deforestation. 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. This report sets out the work undertaken for the project ‘Data trusts in climate’, completed between November 2021 and March 2022. The project was commissioned by the Global Partnership on Artificial Intelligence (GPAI) and delivered in partnership by the Open Data Institute (ODI) and Aapti Institute, with support from the Data Trusts Initiative. It consisted of a literature review, expert interviews and a co-design process involving more than 50 organisations from around the world. We articulate a design for a London Cycling Data Trust, describing how it could interact with the cycling community, be legally incorporated, the technologies it could use and its options for funding. We found that data trusts will be less feasible in other contexts and difficult to apply for a variety of reasons, including ones of cultural, technological and economic nature. We set out lessons learned from exploring data trusts for small shareholder farming in India and for indigenous climate migration in Peru. This work has produced a set of generic feasibility criteria for data trusts, which are intended for use by policymakers and practitioners seeking to understand where data trusts may be necessary and possible, in climate change and beyond. It also sets out a practical roadmap for the development of a London Cycling Data Trust and other similar data trusts, outlining the steps required to implement it in the real world. We also discuss other approaches to responsible data stewardship for AI, and options for GPAI and other policymakers interested in this agenda. As in all progress around new approaches to data stewardship, it is essential to test these ideas further. This project is another step forward for data trusts, but much more is required to unlock their potential.
RIS
TY - GEN TI - Enabling Data Sharing for Social Benefit Through Data Trusts AU - The Global Partnership on AI DA - 2022/03/01 ID - publications/enabling-data-sharing-for-social-benefit-through-data-trusts L1 - https://gpai.ai/projects/data-governance/data-trusts-in-climate-interim-report.pdf UR - /publications/enabling-data-sharing-for-social-benefit-through-data-trusts AB - According to the World Meteorological Organization, 2019 concluded a decade of exceptional global heat, retreating ice and record sea levels driven by greenhouse gases produced by human activities. Data and artificial intelligence have a vital role to play in helping us understand and tackle this climate crisis, from predicting extreme weather events, to improving the energy efficiency of our homes and helping to identify deforestation. 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. This report sets out the work undertaken for the project ‘Data trusts in climate’, completed between November 2021 and March 2022. The project was commissioned by the Global Partnership on Artificial Intelligence (GPAI) and delivered in partnership by the Open Data Institute (ODI) and Aapti Institute, with support from the Data Trusts Initiative. It consisted of a literature review, expert interviews and a co-design process involving more than 50 organisations from around the world. We articulate a design for a London Cycling Data Trust, describing how it could interact with the cycling community, be legally incorporated, the technologies it could use and its options for funding. We found that data trusts will be less feasible in other contexts and difficult to apply for a variety of reasons, including ones of cultural, technological and economic nature. We set out lessons learned from exploring data trusts for small shareholder farming in India and for indigenous climate migration in Peru. This work has produced a set of generic feasibility criteria for data trusts, which are intended for use by policymakers and practitioners seeking to understand where data trusts may be necessary and possible, in climate change and beyond. It also sets out a practical roadmap for the development of a London Cycling Data Trust and other similar data trusts, outlining the steps required to implement it in the real world. We also discuss other approaches to responsible data stewardship for AI, and options for GPAI and other policymakers interested in this agenda. As in all progress around new approaches to data stewardship, it is essential to test these ideas further. This project is another step forward for data trusts, but much more is required to unlock their potential. ER -
APA
The Global Partnership on AI. (2022). Enabling Data Sharing for Social Benefit Through Data Trusts. Available from /publications/enabling-data-sharing-for-social-benefit-through-data-trusts.