IEOR-DRO Seminar: Cynthia Rudin (Duke)

May 9, 2017 | 1:10pm - 2:10pm

IEOR-DRO Seminar: Cynthia Rudin (Duke)

Mudd Hall 303

Title: Recent work on Interpretable Machine Learning
 
Abstract:
This issue of interpretability in predictive modeling is particularly important, given that the US government currently pays private companies for black box predictions that are used throughout the US Justice System. Do we really trust a black box model to make decisions on criminal justice? Propublica showed that we should not. In particular, the black box predictions purchased by the US government are potentially biased. The US government could have tried to prove that no white box (interpretable) model exists that has the same accuracy, but they did not attempt that. For decisions of this gravity - for justice standards, healthcare, energy reliability or other critical infrastructure standards - it should be proven that no sufficient interpretable model exists before resorting to a black box.


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