xAI

Learning Objectives

By the end of this session, learners will be able to:

• understand what Explainable AI (xAI) is, explain the concept, and recognize why it is particularly important in life sciences
• identify relevant xAI techniques for selected real-world scenarios (e.g., lab practice, clinical medicine, pharmacy)

Lectures and Materials

• Introduction to xAI: The “black box” problem and why transparency is crucial for trust in life sciences and healthcare
• xAI in Practice: Real-life examples of xAI techniques used to interpret biological data and clinical models
• Interpreting Explanations: What xAI really tells us vs. what we think it tells us
• Safety and Reliability: Can we trust xAI? Vulnerabilities and the illusion of explanation

Contributors

• Ivan Cucchi
• Marcin Kierczak