  In budget-constrained settings aimed at mitigating unfairness, like law
enforcement, it is essential to prioritize the sources of unfairness before
taking measures to mitigate them in the real world. Unlike previous works,
which only serve as a caution against possible discrimination and de-bias data
after data generation, this work provides a toolkit to mitigate unfairness
during data generation, given by the Unfair Edge Prioritization algorithm, in
addition to de-biasing data after generation, given by the Discrimination
Removal algorithm. We assume that a non-parametric Markovian causal model
representative of the data generation procedure is given. The edges emanating
from the sensitive nodes in the causal graph, such as race, are assumed to be
the sources of unfairness. We first quantify Edge Flow in any edge X -> Y,
which is the belief of observing a specific value of Y due to the influence of
a specific value of X along X -> Y. We then quantify Edge Unfairness by
formulating a non-parametric model in terms of edge flows. We then prove that
cumulative unfairness towards sensitive groups in a decision, like race in a
bail decision, is non-existent when edge unfairness is absent. We prove this
result for the non-trivial non-parametric model setting when the cumulative
unfairness cannot be expressed in terms of edge unfairness. We then measure the
Potential to mitigate the Cumulative Unfairness when edge unfairness is
decreased. Based on these measurements, we propose the Unfair Edge
Prioritization algorithm that can then be used by policymakers. We also propose
the Discrimination Removal Procedure that de-biases a data distribution by
eliminating optimization constraints that grow exponentially in the number of
sensitive attributes and values taken by them. Extensive experiments validate
the theorem and specifications used for quantifying the above measures.
