In many citizen participation projects, the main challenge isn’t to gather citizens’ input: it’s to analyse it. In recent years, social media and new technologies have made it easier than ever for governments and organisations to reach out to citizens and consult them on any given topic. As the amount of collected information grows, it is however becoming increasingly difficult to process this data, meaning that useful insights are getting lost in the process.
At CitizenLab, we believe that helping governments process and analyse citizen contributions is an essential part of citizen engagement projects. Easing up analysis would not only save governments both time and money: it would also help them tap into the collective intelligence of their constituents and make better decisions. This, in turn, could increase trust and encourage dialogue between governments and their citizens.
In order to make this a reality, CitizenLab is using artificial intelligence on its citizen participation platform. This technology, based on machine learning and Natural Language Understanding (NLU) techniques, translates large amounts of unstructured citizen input (ideas, comments, votes) into actionable policy recommendations, optimised for user acceptance by public servants to fit it into their workflows. Once ideas are submitted, they get automatically classified, grouped together or geo-referenced. Administrators of the platform can see at a glance what topics citizens are discussing, how the topics differ across different demographic groups, and how conversations are located around the town or region. It could be that in a neighbourhood, older citizens are asking for better roads whilst their younger counterparts want more public transportation.
With reliable data at their fingertips, policy-makers are better equipped to make decisions and to design policies that truly respond to their citizens’ needs. In the long run, having more reliable and transparent decision processes will strengthen democracy as a whole.