Problem

AI systems are expected to be transparent and explainable. There is a lot of research on how to do that, but applying it in practice is another story. It is also complicated to explain a system in the right way to different actors with different expectations of the system. How do you ensure that you not only explain your system but also that all users understand the system enough to trust it and be able to do something with it in their environment of use?

Outcome of the workshop

• Joint reflection on the impact and needs of different actors and how to respond to them;
• 3 elaborated ideas to make the application explainable to the actors.

What we do

A staff member of the Knowledge Centre guides you and your team to discuss the application you are developing, who plays a role in it, and how explainability can be built in. For the best insights, we recommend involving as many of the disciplines working on the product as possible in this workshop, so you can jointly create a vision about the explainability of the application.

Time and costs

In two workshops of together 5 hours, we go through the application, the actors, the impact and work on the ideas for improved explainability.