Purpose

Improved access to key information/data; Reduction of online disinformation; Improved citizen participation; Increased citizen engagement.

Why we embarked on the project

In 2018, in the build-up to the 2019 elections, we began thinking of the best ways citizens can get easier access to key information and data. This is because we know that during every electoral cycle, citizens are very particular about accessing information related to campaign finance and spending, agendas and manifestos, voter registration, voting procedures, Permanent Voter’s Card (PVC) distribution, and more.

They usually have concerns about electoral violence, ballot box snatching, and where their polling stations would be. Beyond this, election time is a time where mis- and misinformation is know to spread in abnormal amounts. To address these issues, we decided on the Voteron project.

Our strategy and process

First, we considered the already existing data in the public domain, both those open and not, and how we could simplify access to them. We collected data relevant to our goal (data from previous elections especially), and began re-structuring them in a way the technology we want to build would deliver them in simplified, and storied narratives, making cumbersome data easier to read and interpret by non-technical citizenry.

The tool we decided to build was called ‘Vote:ron’, which had robotic assistance and automation abilities. Using Natural Language Processing, a technique in Artificial Intelligence, Voteron was trained to understand human conversational flow, and how to play around with data.

As a result, it could simplify huge datasets into simplified narratives or snippets which can be delivered to any user of the app as push notifications or upon request by the user by asking simple questions in a chat-like nature. Users could also get simplified versions of other relevant information to keep them informed before, during and after the elections.

Outcomes and learnings

We built a tool capable of delivering simplified data and key information in an almost human conversational pattern. We gave citizens access to information in a whole new way. Citizens had a tool that they could easily consult to dispel suspected mis- or disinformation.

Being that it was the first time we were building something around artificial intelligence, we struggled, especially trying to surmount limitations already existing in the technology libraries we employed. We built this tool twice using two different frameworks because of the limitations we had in the first one. The second time we involved a technology partner which gave us a better advantage.

Project partners

AI Envoy and Robotics.