Geolocation of contributions

Citizen ideas are automatically placed on a map

Unlocking the potential of crowdsourcing for public decision-making with artificial intelligence

In citizen participation projects, analysing contributions is often a huge challenge for administrations.
CitizenLab has developed machine-learning algorithms in order to help civil servants easily process thousands of citizen contributions and efficiently use these insights in decision-making.
The dashboards on our platform classify ideas, show what topics are emerging, summarise trends and cluster similar contributions by theme, demographic trait or location.

Innovation Summary

Innovation Overview

Digital participation platforms are important tools for increasing citizen engagement and improving government responsiveness. However, analysing the high volumes of citizen input collected on these platforms is extremely time-consuming and daunting for city officials; this technical difficulty can keep them from uncovering valuable learnings. Setting up a digital participation platform therefore isn’t enough: it’s also necessary to make data analysis more accessible so that civil servants can tap into collective intelligence and make better informed decisions.

The challenge of automation we have been faced with as a civic tech company is shared by the public sector at large. Deloitte recently released a report on AI-augmented governments, in which they conclude that natural language processing could help free up 1,2 billion hours of work and save up to $41,1 billion per year for governments worldwide. The UK government —recognised as the reference in terms of digital government— lined out in its 2020 strategy that a better understanding of citizen needs, based on data and evidence, is the absolute priority for next-gen governments. The three key components in their digital transformation are improved online citizen-facing services, improved efficiency to deliver citizen service across channels, and more effective digitally-enabled collaboration internally. BCG also reports that AI will improve the efficiency of democracy as governments start to ingest all available data to build a fine-grained representation of citizens and adapt public policy accordingly. These are all very positive directions; however, in reality, there is a huge gap with these objectives and the reality of under-resourced and under-staffed public administrations.

CitizenLab aims to bridge the knowledge gap that currently exists in the public sector. Most small to medium administrations understand the need for better work processes and large-scale data analysis, but don’t have the tools, means or in-house knowledge to build custom solutions. We aim to empower civil servants and provide them with machine-learning augmented processes that will help them analyse citizen input, make better decisions, and collaborate more efficiently internally.
Now for the technical details. Over the past year, CitizenLab has developed its own NLP (Natural Language Processing) techniques, with the capacity to automatically classify and analyse thousands of contributions collected on citizen participation platforms. The algorithms identify the main topics and group similar ideas together into clusters, which it is then possible to break down by demographic trait or geographic location. The artificial intelligence is able to process ideas regardless of the language, and works for multi-lingual platforms. The platform administrators have access to all of this information at a glance in intelligent, real-time dashboards. The topic modelling makes it easy to see what the citizen’s priorities are, and to make decisions accordingly. It helps public servants understand what citizens need: for instance, it happens that cities launch a consultation on environment, but what actually comes up in the comments are concerns about mobility and taxes. Being able to break this down by demographic groups and location also gives administrators a better overview of how priorities vary: it can be that a certain neighbourhood prioritises better roads, but its neighbour needs more traffic stops.

We believe that both governments and citizens benefit from this innovation. By automating the time-consuming task of data analysis, our platforms free up time for administrations to meaningfully engage with citizens. It gives them a better understanding of what citizens want and what they prioritise, which in turn leads to better-informed decisions. From the citizens’ perspective, this open and transparent process encourages trust, increases support of policy-decisions, and has a positive impact on the willingness to participate.
Our technology has been deployed to all our existing participation platforms, and is now actively being used by some of our clients. It has made a real impact on the way that they process insights, and has given them more confidence to use and share the findings of the platform. The time gain offered by the automated analysis and reporting has also allowed them to spend more time interacting with citizens and working to implement the ideas.

The next steps are to increase adoption of the feature and to make sure that all of our clients are making the best use of their automated dashboards. In the longer term, this technology could be applied to larger scale conversations such as social media, public forums or other places for online debate. The recent case of the Grand Débat in France has shown how important this technology is: without relevant and trustworthy data analysis, there can be no meaningful large-scale debate and citizen participation.

Innovation Description

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Year: 2018
Organisation Type: Private Sector

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