23 augustus 2023
Lecture: Introduction General Explainable AI
Understand, analyze and elaborate upon the importance of XAI in the modern world. Differentiate between transparent and opaque machine learning models. Categorize and discuss approaches to explainability XAI based on model scope, agnosticity, data types and explanation techniques. Discern, investigate and discuss the trade-off between accuracy and interpretability. Summarize and understand the working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanations. Expand on possible applications of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.