Auditing Deep Neural Networks to Understand Recidivism Predictions

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2016
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Haverford College. Department of Computer Science
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eng
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Open Access
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Abstract
In recent years, deep neural network models have proven to be incredibly accurate on many classification benchmarks. Due to this high accuracy, many non-technical fields are interested in using these models to assist in decision making processes. However, this curiosity is generally tempered by the realization that it is di fficult to understand what features of the data contribute to the prediction. We present a method to evaluate the effect of each feature in a data set on the predictions of a model, which we refer to as gradient feature auditing (GFA). To test this method, we trained four models (a deep neural network, SVM, SLIM, and decision tree) on recidivism data and then applied GFA to each model. The experimental portion verified the ability of GFA to obtain a ranked ordering of features. Next, we attempted to use methods from interpretable learning to validate our procedure. Overall, GFA allows domain experts to use the most effective model of their data in the decision making process, while also retaining the ability to explain how those decisions are being made.
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