Loading...

"Why Should I Trust you?" Explaining the Predictions of Any Classifier

6,630 views

Loading...

Loading...

Transcript

The interactive transcript could not be loaded.

Loading...

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Oct 10, 2016

Author:
Marco Tulio Ribeiro, Department of Computer Science and Engineering, University of Washington

Abstract:
Despite widespread adoption, machine learning models re- main mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.

In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.

More on http://www.kdd.org/kdd2016/

KDD2016 Conference is published on http://videolectures.net/

Loading...


to add this to Watch Later

Add to

Loading playlists...