Artificial intelligence is inspiring potential new applications across the life sciences – from diagnosing disease in medical images to predicting clinical outcomes from tabular health records. But as AI models grow more powerful, they also grow more opaque. In high-stakes domains such as life sciences and healthcare, a prediction alone is not enough: we need to understand why a model made it, whether it can be trusted, and whether it learned the right thing or a shortcut.

In this workshop, we will give an introduction to explainable AI (xAI) and its role in the life sciences. Through interactive lectures and hands-on notebooks, participants will apply explanation methods such as SHAP, feature importance, saliency maps, and Grad-CAM to real medical datasets – including brain stroke prediction from tabular data and classification of chest X-ray images. By the end of the workshop, you will have hands-on experience in generating, interpreting, and critically validating AI explanations on real-life science problems