be hired more/less likely than women • Extreme measure: hire 50/50% of men/women • The extreme measure may cause individual discrimination • Women with the same skill may be more/less likely hired than men
employment • This can be legitimate • Full-time employment may indicate higher skill • But can adversely affect women • Women tend to have more part-time job • Family responsibility, child birth, etc...
of gender if an applicant has the same skill • Fairness against indirect discrimination • If 25% of applicants are women, 25% of hired applicants are also women (regardless of relative skill level between men/women)
but women by the length of hair • Hire men randomly, but women by their skill • Probability can be same • More generally, it is based on statistical properties • address “disparate impact” • do not address “disparate treatment”
S) • Y : outcome (Y = 0 or 1) • d : similarity measure on V (real number) • d(x, y) >= 0, d(x, y) = d(y, x), d(x, x) = 0 • M : Assignment for Y to V (M : V -> distributions on Y) • may use lottery (eg. choosing jury) • D : measure of difference between two distribution on Y
= 1 for an individual belonging to S • : the largest for any M with (D, d) - Lipschitz property • Theorem: • : Earth mover’s distance between S and T μS (1) biasD,d (S, T) μS (1) − μT (1) biasD,d (S, T) ≤ dEM (S, T) dEM (S, T)
popular views) • If it causes disadvantage to one of the groups* • If it shows/expresses exclusion of one of the groups* • If it is not justiﬁed rationally • If it is based on one’s nature which cannot be changed
Through Awareness, Dwork et al., ITCS 2012 • FlipTest: Fairness Testing via Optimal Transport, Black et al. ACM FAT 2020 • Altman, "Discrimination", The Stanford Encyclopedia of Philosophy (Winter 2016 Edition)