Does Representation Guarantee Welfare?
Jakob de Raaij, Ariel D. Procaccia, Alexandros Psomas
摘要
A panel satisfies descriptive representation when its composition reflects the population. We examine the role of descriptive representation in collective decision making through an optimization lens, asking whether representative panels make decisions that maximize social welfare for the underlying population. Our main results suggest that, in general, representation with respect to intersections of two or more features guarantees higher social welfare than that achieved by the status quo of proportionally representing individual features. Moreover, an analysis of real data suggests that representation with respect to pairs of features is feasible in practice. These results have significant implications for the design of citizens' assemblies, which are gaining prominence in AI governance.
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它引用的顶会 Paper7
- Neutralizing Self-Selection Bias in Sampling for SortitionBailey Flanigan, Paul Gölz, Anupam Gupta, Ariel D. ProcacciaNeurIPS 2020 · 被引用 44 次
- An Axiomatic Theory of Provably-Fair Welfare-Centric Machine LearningCyrus CousinsNeurIPS 2021 · 被引用 39 次
- Fair Sortition Made TransparentBailey Flanigan, Gregory Kehne, Ariel D. ProcacciaNeurIPS 2021 · 被引用 28 次
- Manipulation-Robust Selection of Citizens' AssembliesBailey Flanigan, Jennifer Liang, Ariel D. Procaccia, Sven WangAAAI 2024 · 被引用 15 次
- Learning Social Welfare FunctionsKanad Pardeshi, Itai Shapira, Ariel D. Procaccia, Aarti SinghNeurIPS 2024 · 被引用 9 次
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