Learning to Ignore: Fair and Task Independent Representations

TL;DR

This paper proposes a unified framework for learning invariant representations that promote fairness, domain adaptation, and effective learning from limited data by enforcing invariance to sensitive attributes through a regularizer.

Contribution

It introduces a simple regularizer that enforces invariant feature representations, unifying fairness, domain adaptation, and few-shot learning under a common approach.

Findings

Effective in learning fair models with interpretable representations

Improves domain transfer and adaptation with minimal data

Enforces invariance to sensitive attributes across tasks

Abstract

Training fair machine learning models, aiming for their interpretability and solving the problem of domain shift has gained a lot of interest in the last years. There is a vast amount of work addressing these topics, mostly in separation. In this work we show that they can be seen as a common framework of learning invariant representations. The representations should allow to predict the target while at the same time being invariant to sensitive attributes which split the dataset into subgroups. Our approach is based on the simple observation that it is impossible for any learning algorithm to differentiate samples if they have the same feature representation. This is formulated as an additional loss (regularizer) enforcing a common feature representation across subgroups. We apply it to learn fair models and interpret the influence of the sensitive attribute. Furthermore it can be used for domain adaptation, transferring knowledge and learning effectively from very few examples. In all applications it is essential not only to learn to predict the target, but also to learn what to ignore.