165 citations · 225 across the 3 of their papers we have counts for
3 papers · 1 filter
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
Julius Adebayo, Justin Gilmer, Ian Goodfellow +1
Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation method…
Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly +3
Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed…
Investigating Human + Machine Complementarity for Recidivism Predictions
Sarah Tan, Julius Adebayo, Kori Inkpen +1
When might human input help (or not) when assessing risk in fairness domains? Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of defendants in the ProPu…