  Law enforcement must prioritize sources of unfairness before mitigating their
underlying unfairness, considering that they have limited resources. Unlike
previous works that only make cautionary claims of discrimination and de-biases
data after its generation, this paper attempts to prioritize unfair sources
before mitigating their unfairness in the real-world. We assume that a causal
bayesian network, representative of the data generation procedure, along with
the sensitive nodes, that result in unfairness, are given. We quantify Edge
Flow, which is the belief flowing along an edge by attenuating the indirect
path influences, and use it to quantify Edge Unfairness. We prove that
cumulative unfairness is non-existent in any decision, like judicial bail,
towards any sensitive groups, like race, when the edge unfairness is absent,
given an error-free linear model of conditional probability. We then measure
the potential to mitigate the cumulative unfairness when edge unfairness is
decreased. Based on these measures, we propose an unfair edge prioritization
algorithm that prioritizes the unfair edges and a discrimination removal
procedure that de-biases the generated data distribution. The experimental
section validates the specifications used for quantifying the above measures.
