  Fairness is becoming a rising concern w.r.t. machine learning model
performance. Especially for sensitive fields such as criminal justice and loan
decision, eliminating the prediction discrimination towards a certain group of
population (characterized by sensitive features like race and gender) is
important for enhancing the trustworthiness of model. In this paper, we present
a new general framework to improve machine learning fairness. The goal of our
model is to minimize the influence of sensitive feature from the perspectives
of both the data input and the predictive model. In order to achieve this goal,
we reformulate the data input by removing the sensitive information and
strengthen model fairness by minimizing the marginal contribution of the
sensitive feature. We propose to learn the non-sensitive input via sampling
among features and design an adversarial network to minimize the dependence
between the reformulated input and the sensitive information. Extensive
experiments on three benchmark datasets suggest that our model achieve better
results than related state-of-the-art methods with respect to both fairness
metrics and prediction performance.
