Logic for Explainable AI - Tutorial
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Dive into a comprehensive tutorial on Logic for Explainable AI presented by the UCLA Automated Reasoning Group. Explore three key dimensions of understanding decisions made by learned classifiers: characterizing necessary and sufficient conditions, identifying maximal irrelevant aspects, and determining minimal perturbations for alternate decisions. Learn about a semantical and computational theory of explainability based on recent developments in symbolic logic, applicable to non-symbolic classifiers like Bayesian networks, decision trees, random forests, and certain neural networks. Discover how to represent classifiers using tractable circuits and class formulas, understand discrete logic versus Boolean logic, and delve into concepts such as sufficient, complete, and necessary reasons for decisions. Examine logical operators for computing instance abstractions, explore beyond simple explanations, and investigate general reasons for decisions. Gain insights into targeting new decisions, selection semantics of complete and general reasons, and compiling classifiers into class formulas from various machine learning models. This expanded version of a live tutorial from the 2023 ACM/IEEE Symposium on Logic in Computer Science offers nearly two hours of in-depth content, complete with introduction, conclusion, and a detailed breakdown of topics. Read more