Abstract:

I will discuss a line of work in which we compile common machine learning systems into symbolic representations that have the same input-output behavior to facilitate formal reasoning about these systems. We have targeted Bayesian network classifiers, random forests and some types of neural networks, compiling each into tractable Boolean circuits, including Ordered Binary Decision Diagrams (OBDDs). Once the machine learning system is compiled into a tractable Boolean circuit, reasoning can commence using classical AI and computer science techniques. This includes generating explanations for decisions, quantifying robustness and verifying properties such as monotonicity. I will particularly discuss a new theory for unveiling the reasons behind the decisions made by classifiers, which can detect classifier bias sometimes from the reasons behind unbiased decisions. The theory is based on a new type of tractable circuits, `Reason Circuits,’ introduced specifically for this purpose.

Biography:

Adnan Darwiche is a professor and former chairman of the computer science department at UCLA. He directs the Automated Reasoning Group, which focuses on probabilistic and symbolic reasoning and their applications to machine learning. Professor Darwiche is Fellow of AAAI and ACM. He is a former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and author of “Modeling and Reasoning with Bayesian Networks,” by Cambridge University Press. His group’s YouTube Channel can be found at: http://www.youtube.com/c/UCLAAutomatedReasoningGroup