Explainable AI is no longer a luxury, but a necessity. As AI systems are increasingly used in decision-making, the need to understand how these systems arrive at their conclusions also grows.

When AI models make decisions that affect people, such as in medical diagnoses or credit assessments, it is crucial that we can explain how these decisions are made. Transparency and explainability are essential to build trust, recognize bias, and make responsible choices.

During this session, we will therefore cover:

• What Explainable and Interpretable AI exactly entail, and why this is relevant
• Info Support’s vision on the importance of XAI
• AutoXPlain: a project in which automatically interpretable models are trained and selected based on both performance and explainability
• The challenges encountered in building AutoXPlain

Target audience: Everyone interested in AI

Prerequisites: None