Editing a classifier by rewriting its prediction rules

Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry

Keywords: model debugging, spurious correlations, robustness

TL;DR: We propose a method that allows users to rewrite high-level predictions rules with virtually no additional data collection.

Abstract: We propose a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. Our method requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new environments, and modifying it to ignore spurious features.

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Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/arxiv:2112.01008/code)

Code: https://github.com/MadryLab/EditingClassifiers