Judea Pearl: "Interpretability and explainability from a causal lens"

Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Interpretability and explainability from a causal lens" Judea Pearl - University of California, Los Angeles (UCLA), Computer Science Abstract: I will describe the task of interpreting and explaining data as seen through the science of cause and effect, and distinguish it from the task of interpreting algorithmic systems. The former calls for a mapping between data and the ropes of reality, the latter between data and the intentions of the system builder. Reference: J. Pearl "The Limitations of Opaque Learning Machines," https://ucla.in/2wj4pox Chapter 2 in John Brockman (Ed.), "Possible Minds: Twenty-Five Ways of Looking at AI" Institute for Pure and Applied Mathematics, UCLA October 16, 2019 For more information: http://www.ipam.ucla.edu/mlpws2