Overview
Syllabus
Introduction
From numeric to symbolic classifiers
Representing classifiers using tractable circuits
Representing classifiers using class formulas
Discrete logic vs Boolean logic
The sufficient reasons for decisions: why a decision was made? aka abductive explanations, PI-explanations
The complete reasons for decisions: instance abstraction
The necessary reasons for decisions: how to change a decision? aka contrastive explanations, counterfactual explanations
Terminology: PI-explanations, abductive explanations, contrastive explanations, counterfactual explanations
A logical operator for computing instance abstractions complete reasons
The first theory of explanation: A summary
Beyond simple explanations: A key insight
The general reasons for decisions: instance abstraction
Complete vs general reasons two notions of instance abstraction
The general sufficient and general necessary reasons for decisions
The second theory of explanation: A summary
Targeting a new decision
Selection semantics of complete and general reasons instance abstractions
Compiling classifiers into class formulas from decision trees, random forests, Bayesian networks, and Binary neural networks
Conclusion
Taught by
UCLA Automated Reasoning Group