  Machine learning algorithms that aid human decision-making may inadvertently
discriminate against certain protected groups. We formalize direct
discrimination as a direct causal effect of the protected attributes on the
decisions, while induced discrimination as a change in the causal influence of
non-protected features associated with the protected attributes. The
measurements of marginal direct effect (MDE) and SHapley Additive exPlanations
(SHAP) reveal that state-of-the-art fair learning methods can induce
discrimination via association or reverse discrimination in synthetic and
real-world datasets. To inhibit discrimination in algorithmic systems, we
propose to nullify the influence of the protected attribute on the output of
the system, while preserving the influence of remaining features. We introduce
and study post-processing methods achieving such objectives, finding that they
yield relatively high model accuracy, prevent direct discrimination, and
diminishes various disparity measures, e.g., demographic disparity.
