Demographic parity is a fairness criterion requiring a model’s classification outcomes to be statistically independent of a specified sensitive attribute. It is satisfied when different demographic groups receive positive outcomes at comparable rates, regardless of differences in other variables.
A fairness metric that is satisfied if the results of a model's classification are not dependent on a given sensitive attribute. For example, if both Lilliputians and Brobdingnagians apply to Glubbdubdrib University, demographic parity is achieved if the percentage of Lilliputians admitted is the same as the percentage of Brobdingnagians admitted, irrespective of whether one group is on average more qualified than the other. Contrast with equalized odds and equality of opportunity, which permit classification results in aggregate to depend on sensitive attributes, but don't permit classification results for certain specified ground truth labels to depend on sensitive attributes. See "Attacking discrimination with smarter machine learning" for a visualization exploring the tradeoffs when optimizing for demographic parity. See Fairness: demographic parity in Machine Learning Crash Course for more information.