# Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

## TL;DR

This paper introduces an algorithm that generates minimal, stable, and symbolic input modifications to explain and influence neural network outputs, aiding user understanding and feedback.

## Contribution

It presents a novel linear constraint-based method for producing interpretable input corrections for neural networks with ReLU activations.

## Key findings

- Effective on three diverse neural network models
- Produces minimal and stable input corrections
- Facilitates user understanding of neural network decisions

## Abstract

We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algorithm generates such a correction by solving a series of linear constraint satisfaction problems. The technique is evaluated on three neural network models: one predicting whether an applicant will pay a mortgage, one predicting whether a first-order theorem can be proved efficiently by a solver using certain heuristics, and the final one judging whether a drawing is an accurate rendition of a canonical drawing of a cat.