Can natural language understanding help encourage quicker political response to citizen views on digital democracy platforms?
This experiment tested whether using Natural Language Understanding (NLU) would make it quicker for public administrators to analyse insights from digital democracy platforms, and therefore increase their responsiveness to citizen ideas. NLU is a technique that allows machines to understand the meaning, subtleties and nuances of human language.
The experiment found that new manually-controlled solutions helped to significantly increase the number of citizen ideas receiving feedback. The experiment did not succeed in fully testing the NLU-assisted solutions within the timeframe of the reporting period. However, insights from seven in-depth interviews with city administrators found that the NLU-assisted clustering feature highlighted trends and needs that cities weren’t aware of.
Many cities operate digital democracy platforms to collect ideas and opinions from citizens. These large collections of unstructured data need to be analysed, which is difficult and time-consuming for city administrators. This can often discourage the use of such data in 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.
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