  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.
