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  4. On Meritocracy in Optimal Set Selection

On Meritocracy in Optimal Set Selection

Author(s)
Thomas Kleine Buening
Meirav Segal
Debabrota Basu
Dimitrakakis, Christos  
Chaire de science des données  
Anne-Marie George
Date issued
February 23, 2021
In
EAAMO 22 : Equity and Access in Algorithms, Mechanisms, and Optimization
From page
1
To page
14
Subjects
Artificial Intelligence (cs.AI)
Abstract
Typically, merit is defined with respect to some intrinsic measure of worth. We instead consider a setting where an individual's worth is \emph{relative}: when a Decision Maker (DM) selects a set of individuals from a population to maximise expected utility, it is natural to consider the \emph{Expected Marginal Contribution} (EMC) of each person to the utility. We show that this notion satisfies an axiomatic definition of fairness for this setting. We also show that for certain policy structures, this notion of fairness is aligned with maximising expected utility, while for linear utility functions it is identical to the Shapley value. However, for certain natural policies, such as those that select individuals with a specific set of attributes (e.g. high enough test scores for college admissions), there is a trade-off between meritocracy and utility maximisation. We analyse the effect of constraints on the policy on both utility and fairness in extensive experiments based on college admissions and outcomes in Norwegian universities.
Publication type
book part
Identifiers
https://libra.unine.ch/handle/20.500.14713/26496
DOI
10.1145/3551624.3555305
-
2102.11932v3
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2102.11932.pdf

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