In the era of complex and powerful machine learning (ML) models, understanding the decision-making process of these models has become more challenging, and different interpretability methods can help us build trust, address bias, and ensure compliance with different standards. The Alibi Explain library offers a comprehensive toolkit for interpreting machine learning models and shed light on their inner workings. No prior experience with the library is required, but some knowledge of machine learning is expected.

During the workshop, we will cover the fundamental concepts and techniques of interpretable machine learning, exploring various explainability methods supported by Alibi Explain. We will discuss techniques such as rule-based explanations, feature importance, and counterfactual explanations. Through hands-on exercises, participants will gain practical experience in interpreting models and understanding their predictions.

Throughout the workshop, we will emphasize real-world applications and use cases to demonstrate the relevance and importance of interpretable machine learning. We will discuss how interpretability can enable better decision-making in domains like finance, healthcare, and retail. By the end of the workshop, attendees will have a good understanding of interpretable machine learning concepts and practical skills for finding an alibi for their ML models.