Neutralized Empirical Risk Minimization with Generalization Neutrality Bound

TL;DR

This paper introduces a novel neutralized empirical risk minimization framework that ensures classifiers are unbiased with respect to a viewpoint, providing theoretical bounds and a practical neutral SVM algorithm with improved performance.

Contribution

It proposes a new NERM framework for unbiased classification, deriving theoretical neutrality bounds and implementing a neutral SVM with enhanced accuracy.

Findings

Neutral SVM maintains neutrality guarantees while improving classification accuracy.

Theoretical bounds on empirical and generalization neutrality risks are established.

Experimental results demonstrate the effectiveness of the neutral SVM on real datasets.

Abstract

Currently, machine learning plays an important role in the lives and individual activities of numerous people. Accordingly, it has become necessary to design machine learning algorithms to ensure that discrimination, biased views, or unfair treatment do not result from decision making or predictions made via machine learning. In this work, we introduce a novel empirical risk minimization (ERM) framework for supervised learning, neutralized ERM (NERM) that ensures that any classifiers obtained can be guaranteed to be neutral with respect to a viewpoint hypothesis. More specifically, given a viewpoint hypothesis, NERM works to find a target hypothesis that minimizes the empirical risk while simultaneously identifying a target hypothesis that is neutral to the viewpoint hypothesis. Within the NERM framework, we derive a theoretical bound on empirical and generalization neutrality risks. Furthermore, as a realization of NERM with linear classification, we derive a max-margin algorithm, neutral support vector machine (SVM). Experimental results show that our neutral SVM shows improved classification performance in real datasets without sacrificing the neutrality guarantee.