TY - GEN
T1 - Trade-offs between Group Fairness Metrics in Societal Resource Allocation
AU - Mashiat, Tasfia
AU - Gitiaux, Xavier
AU - Rangwala, Huzefa
AU - Fowler, Patrick
AU - Das, Sanmay
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/6/21
Y1 - 2022/6/21
N2 - We consider social resource allocations that deliver an array of scarce supports to a diverse population. Such allocations pervade social service delivery, such as provision of homeless services and assignment of refugees to cities, among others. At issue is whether allocations are fair across sociodemographic groups and intersectional identities. Our paper shows that necessary trade-offs exist for fairness in the context of scarcity; many reasonable definitions of equitable outcomes cannot hold simultaneously except under stringent conditions. For example, defining fairness in terms of improvement over a baseline inherently conflicts with defining fairness in terms of loss compared with the best possible outcome. Moreover, we demonstrate that the fairness trade-offs stem from heterogeneity across groups in intervention responses. Administrative records on homeless service delivery offer a real-world example. Building on prior work, we measure utilities for each household as the probability of reentry into homeless services if given three homeless services. Heterogeneity in utility distributions (conditional on received services) for several sociodemographic groups (e.g. single women with children versus without children) generates divergence across fairness metrics. We argue that such heterogeneity, and thus, fairness trade-offs, pervade many social policy contexts.
AB - We consider social resource allocations that deliver an array of scarce supports to a diverse population. Such allocations pervade social service delivery, such as provision of homeless services and assignment of refugees to cities, among others. At issue is whether allocations are fair across sociodemographic groups and intersectional identities. Our paper shows that necessary trade-offs exist for fairness in the context of scarcity; many reasonable definitions of equitable outcomes cannot hold simultaneously except under stringent conditions. For example, defining fairness in terms of improvement over a baseline inherently conflicts with defining fairness in terms of loss compared with the best possible outcome. Moreover, we demonstrate that the fairness trade-offs stem from heterogeneity across groups in intervention responses. Administrative records on homeless service delivery offer a real-world example. Building on prior work, we measure utilities for each household as the probability of reentry into homeless services if given three homeless services. Heterogeneity in utility distributions (conditional on received services) for several sociodemographic groups (e.g. single women with children versus without children) generates divergence across fairness metrics. We argue that such heterogeneity, and thus, fairness trade-offs, pervade many social policy contexts.
KW - algorithmic fairness
KW - fairness metrics
KW - Resource allocation
UR - https://www.scopus.com/pages/publications/85133026189
U2 - 10.1145/3531146.3533171
DO - 10.1145/3531146.3533171
M3 - Conference contribution
AN - SCOPUS:85133026189
T3 - ACM International Conference Proceeding Series
SP - 1095
EP - 1105
BT - Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
PB - Association for Computing Machinery
T2 - 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Y2 - 21 June 2022 through 24 June 2022
ER -