TY - GEN
T1 - A just approach balancing rawlsian leximax fairness and utilitarianism
AU - Chen, Violet Xinying
AU - Hooker, J. N.
N1 - Publisher Copyright:
© 2020 Copyright held by the owner/author(s).
PY - 2020/2/7
Y1 - 2020/2/7
N2 - Numerous AI-assisted resource allocation decisions need to balance the conflicting goals of fairness and efficiency. Our paper studies the challenging task of defining and modeling a proper fairness-efficiency trade off. We define fairness with Rawlsian leximax fairness, which views the lexicographic maximum among all feasible outcomes as the most equitable; and define efficiency with Utilitarianism, which seeks to maximize the sum of utilities received by entities regardless of individual differences. Motivated by a justice-driven trade off principle: prioritize fairness to benefit the less advantaged unless too much efficiency is sacrificed, we propose a sequential optimization procedure to balance leximax fairness and utilitarianism in decision-making. Each iteration of our approach maximizes a social welfare function, and we provide a practical mixed integer/linear programming (MILP) formulation for each maximization problem. We illustrate our method on a budget allocation example. Compared with existing approaches of balancing equity and efficiency, our method is more interpretable in terms of parameter selection, and incorporates a strong equity criterion with a thoroughly balanced perspective.
AB - Numerous AI-assisted resource allocation decisions need to balance the conflicting goals of fairness and efficiency. Our paper studies the challenging task of defining and modeling a proper fairness-efficiency trade off. We define fairness with Rawlsian leximax fairness, which views the lexicographic maximum among all feasible outcomes as the most equitable; and define efficiency with Utilitarianism, which seeks to maximize the sum of utilities received by entities regardless of individual differences. Motivated by a justice-driven trade off principle: prioritize fairness to benefit the less advantaged unless too much efficiency is sacrificed, we propose a sequential optimization procedure to balance leximax fairness and utilitarianism in decision-making. Each iteration of our approach maximizes a social welfare function, and we provide a practical mixed integer/linear programming (MILP) formulation for each maximization problem. We illustrate our method on a budget allocation example. Compared with existing approaches of balancing equity and efficiency, our method is more interpretable in terms of parameter selection, and incorporates a strong equity criterion with a thoroughly balanced perspective.
KW - Distributive Justice
KW - Fairness
KW - Trade off
KW - Utilitarianism
UR - http://www.scopus.com/inward/record.url?scp=85082170917&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082170917&partnerID=8YFLogxK
U2 - 10.1145/3375627.3375844
DO - 10.1145/3375627.3375844
M3 - Conference contribution
AN - SCOPUS:85082170917
T3 - AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
SP - 221
EP - 227
BT - AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
T2 - 3rd AAAI/ACM Conference on AI, Ethics, and Society, AIES 2020, co-located with AAAI 2020
Y2 - 7 February 2020 through 8 February 2020
ER -