Skip to main content

Crowdsourcing

Application

Problem: 

The system has insufficient or low confidence in the data, it relies on explicit user inputs to make effective, real-time updates to the AI predictions. The user doesn’t want to put a lot of effort into this.

The Trainline app asks for user input to accurately show seat availability on board.
The Trainline app uses crowdsourcing to help make predictions around seat availability, keeping the interactions light so users are more inclined to participate.

Solution:

The system provides easy input options for users to contribute back (by choice) to the functioning of the AI and highlights the reasons for doing so.

Discussion:

Many algorithms rely on ongoing user updates to the information they work from in order to be effective. While some do this in the background (think Google Maps or Waze and their dependence on regular traffic updates, largely gleaned from the relative speed of drivers over a distance, to accurately predict travel times), other algorithms may need users to actively give feedback. By making their input both easy to offer and the reasons for doing so clear, the AI systems can bring the user into the process to ensure the outcomes are useful for all users.

 

Pattern submission via Jan Srutek