Zeynep Akata: To Trust AI, We Need to Understand What Goes on Behind the Scenes

How can we build AI systems that are transparent enough to be trusted with decisions that affect our health and daily lives?

MCML PI Zeynep Akata explains why bias in foundation models is a critical challenge and why explainability should guide model development rather than just auditing it. By identifying which model components are responsible for flawed decisions and adjusting them, researchers can steer AI toward more trustworthy behaviour.

In this video series, discover how MCML researchers are bridging theory and practice to build AI systems that prioritize accessibility, sovereignty, and impact.

The film was produced and edited by Nicole Huminski and Nikolai Huber.