Can natural language understanding and machine learning technologies help encourage quicker political response to citizen views on digital democracy platforms?
The experiment aims to improve policy makers’ uptake of citizen-generated ideas and insights from digital democracy platforms. It will test the ability of Natural Language Understanding (NLU) and Machine Learning technologies to translate unstructured citizen inputs (ideas, arguments, votes) on the CitizenLab platform into actionable policy recommendations. NLU is a technique that allows machines to understand the meaning, subtleties and nuances of human language. The experiment will also explore how best to incorporate these NLU-generated insights within the workflows of public servants to maximise its acceptance and use. This experiment will measure how quickly local governments across six cities process and respond to citizen input.
Digital democracy platforms are important new tools for increasing citizen engagement and improving government responsiveness. However, analysing high volumes of citizen input from digital democracy platforms is extremely time-consuming for city officials. This can discourage them from taking into account the data and tacit knowledge from citizens in their decision-making processes. Making it quicker for public officials to analyse insights from digital democracy platforms, and incorporate at the optimal point in policy-making should increase the use of this collective intelligence and result in better public decisions.
This experiment will increase knowledge on how to make better use of inputs generated generated through collective intelligence methods. The findings will be relevant for other digital democracy initiatives, for governments, companies, and other institutions wanting to collaborate with crowds.