Right for the Right Reason: Training Agnostic Networks

TL;DR

This paper addresses the challenge of training neural networks that make accurate predictions without relying on protected or undesired concepts, using domain-adversarial techniques to ensure models are 'agnostic' to such concepts.

Contribution

It introduces a method to train neural networks that are explicitly agnostic to protected concepts, leveraging domain-adversarial training to remove implicit biases.

Findings

Successfully removes protected concept information from models.

Ensures models make decisions without relying on undesired attributes.

Demonstrates effectiveness across different protected concepts.

Abstract

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.