  Fairness in algorithmic decision-making processes is attracting increasing
concern. When an algorithm is applied to human-related decision-making an
estimator solely optimizing its predictive power can learn biases on the
existing data, which motivates us the notion of fairness in machine learning.
while several different notions are studied in the literature, little studies
are done on how these notions affect the individuals. We demonstrate such a
comparison between several policies induced by well-known fairness criteria,
including the color-blind (CB), the demographic parity (DP), and the equalized
odds (EO). We show that the EO is the only criterion among them that removes
group-level disparity. Empirical studies on the social welfare and disparity of
these policies are conducted.
