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Methods
Policy making steps
Contributors
Naima Lipka
Scientist & Project Coordinator at Potsdam Institute for Climate Impact Research
Related ressources
Traditional efforts to include citizens in local policy making rely on physical town hall meetings – with a small number of invited citizens – and a call to review and provide feedback on pre-formulated policies.
But this approach often leaves out valuable insights and produces sub-optimal policies.
Instead, the Danish municipality of Slagelse decided to try a new augmented approach to informing policy makers using AI. The project aimed to transform how citizens get involved by including them earlier and more continuously by getting them to participate in policy formulation as opposed to being simple reviewers,and to include more and different citizens, who share their thoughts and suggestions in novel ways with policymakers.
Slagelse realised that it was facing an increase in its prerogatives but was not engaging sufficiently with its citizens. Sparked by Frédéric Laloux’s book Reinventing Organisation, Slagelse aimed to “create the best possible municipality to live in” guided by one principle: the best conditions for everyone in the municipality would be achieved by harnessing citizens’ knowledge and creativity.
The project used a crowdsourcing approach to provide ongoing ideas to shape local health and well-being policies and to generate and collect suggestions on how to improve local public health services in Slagelse municipality. The project asked citizens questions like “how Slagelse the municipality become the healthiest municipality in Denmark.”
The city of Slagese started partnered up with the Collective Intelligence Research Group at the IT University of Copenhagen to develop a “citizen-sourcing” research project over 4 years, also using AI methods from the CitizenLab civic tech platform.
The platform uses Natural Language Processing to gather and sort citizens’ arguments, insights and proposals. This input in turn provides digestible, rich and quantitative input to the policymakers.
AI and Natural Language Processing (NLP):
NLP is a type of Artificial Intelligence (AI) that helps to quickly analyse vast amounts of text to extract trends and insights.
Crowdsourcing and tacit knowledge:
In this project, the goal is to elicit valuable tacit knowledge that is usually difficult to access within a community: knowledge that is difficult to make explicit (write down or verbalise). This approach to uncovering valuable untapped insights takes its origins in Michael Polanyi’s definition of tacit knowledge as “knowledge accumulated from everyday experience”.
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