Civic AI Toolkit: Collective climate action blueprint

www.nesta.org.uk/toolkit/civicai-climate-action/
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Civic AI Toolkit: Collective climate action blueprint

Responding to the climate crisis will require radical changes to how we live, but sustained change requires actions that are locally appropriate and guided by an understanding of the benefits of taking action (and consequences of not taking action).

For climate action to take root and happen at the speed and scale required, people and communities must be empowered to act now. The recent UK citizen’s climate assembly highlighted the public’s desire to protect and restore the natural environment. But as an individual, it’s difficult to know which actions may have comparatively more impact and to sustain these lifestyle changes. As a community, diverse values make it difficult to identify areas of consensus to commit to collective goals. It is also challenging to imagine the scales and timelines associated with climate change or to make decisions that adequately consider the rights of the natural environment or future generations.

Collective climate action - overview layout.png

This blueprint uses augmented collective intelligence to address the following key challenges:

  • Feedback - providing personal feedback to encourage sustained commitments and the agency to act.
  • Measuring impact - objectively measuring the impact of individual and aggregated actions.
  • Aligning values - incorporating diverse beliefs and perspectives to identify areas of consensus on specific climate actions.

How it works in practice

In this use case AI helps communities to build a shared understanding and align diverse perspectives around common values through tools that support new forms of collective deliberation. For example, AI agents are used to represent the rights of animals, plants or future generations of people at community town halls or citizens assemblies during debates about climate action. Expanding discussions to include these perspectives can help communities to make more ecocentric decisions that take long-term impacts into account. Machines analyse the impact of individual’s lifestyle choices and prompt them to take actions that help reduce their impact (e.g. composting waste), as well as sharing these new commitments with peers in their community (e.g. neighbours, friends). Simulations help to model the potential outcomes of these different actions via an open dashboard and identify gaps between the impact of actions taken by many individuals and what is required to meet national climate targets. In this scenario, machines and peers provide mutual accountability and peer support, helping to motivate individuals to take action.

Setting privacy and data governance standards from the outset and continuously reviewing them throughout implementation will ensure that individual’s privacy is protected when proposing or committing to interventions in the collective climate action scenario. All simulations and models used to measure the potential impact of actions should be technically robust, with attention to reproducibility and transparency.