class: center, middle, big-title # Algorithmic Fairness Overview Predictive Analytics Lab, University of Sussex --- layout: true <!-- set the layout for all following slides --> .header[] .footer[University of Sussex] --- # Fairness definitions - Group fairness - Individual fairness --- # Definitions – Group Fairness ## Demographic Parity Make (positive) predictions at the same rate across different subgroups. $$ P(\hat{Y} = 1 | S=0) = P(\hat{Y} = 1 | S=1) $$ → Equalise acceptance rate accross groups. --- # Definitions – Group Fairness ## Equal Opportunity Make (positive) predictions at the same rate across different subgroups, conditioned on if the known outcome is positive. $$ P(\hat{Y} = 1 | S=0, Y=1) = P(\hat{Y} = 1 | S=1, Y=1) $$ → Equalise the TPR across groups. --- # Individual Fairness > Similar individuals should be treated similarly. Given some distance measure between individuals (e.g. the Euclidean distance in the feature space), the distance between individuals and the distance of their associated outcome should be proportional.