Comparing Fairness Criteria Based on Social Outcome
This work addresses fairness in algorithmic decision-making for individuals, but it is incremental as it compares existing criteria without introducing new methods.
The paper compared fairness criteria like color-blind, demographic parity, and equalized odds, showing that equalized odds is the only one that removes group-level disparity, with empirical studies on social welfare and disparity.
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.