165 citations · 178 across the 2 of their papers we have counts for
5 papers
Explaining Explanations to Society
Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter +1
There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government…
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…
The (Un)reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo +5
Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the m…