  A critical concern in data-driven decision making is to build models whose
outcomes do not discriminate against some demographic groups, including gender,
ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of
the sensitive attributes is essential, while, in practice, these attributes may
not be available due to legal and ethical requirements. To address this
challenge, this paper studies a model that protects the privacy of the
individuals sensitive information while also allowing it to learn
non-discriminatory predictors. The method relies on the notion of differential
privacy and the use of Lagrangian duality to design neural networks that can
accommodate fairness constraints while guaranteeing the privacy of sensitive
attributes. The paper analyses the tension between accuracy, privacy, and
fairness and the experimental evaluation illustrates the benefits of the
proposed model on several prediction tasks.
