A Rule for Committee Selection with Soft Diversity Constraints

Aziz, Haris

doi:10.1007/s10726-019-09634-5

Cited by 13 publications

(17 citation statements)

References 16 publications

(9 reference statements)

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“…When individuals have multiple types, there are two natural conventions, namely one-for-all and one-for-one, on how many reserved seats an individual takes up (to be consistent with the majority of text) or how reserved positions are accounted for [39]. Under the one-for-all convention, an individual takes the reserved seats of all types she satisfies [9,10,22]. For example, an aboriginal girl could take up one seat reserved for girls and one seat reserved for aboriginals.…”

Section: Related Workmentioning

confidence: 99%

“…They used a heuristic to deal with non-nested common quotas and their algorithm does not guarantee a fair outcome. Aziz [9] considers the one-for-all convention for diversity and proposed an algorithm for a choice rule that uses minimum quotas to [16] examined the complexity of multiwinner voting with diversity constraints under the one-for-all convention.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Rank Smart Reserves

Aziz

Sun

2021

Proceedings of the 22nd ACM Conference on Economics and Computation

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We study the school choice problem where each school has flexible multi-ranked diversity goals, and each student may belong to multiple overlapping types, and consumes only one of the positions reserved for their types. We propose a novel choice function and show that it is the unique rule that satisfies three fundamental properties: maximal diversity, non-wastefulness, and justified envy-freeness. We provide a fast polynomialtime algorithm for our choice function that is based on the Dulmage Mendelsohn Decomposition Theorem as well as new insights into the combinatorial structure of constrained rank maximal matchings. Even for the case of minimum and maximum quotas for types (that capture two ranks), ours is the first known polynomial-time approach to compute an optimally diverse choice outcome. Finally, we prove that the choice function we design for schools, satisfies substitutability and hence can be directly embedded in the generalized deferred acceptance algorithm to achieve strategyproofness and stability. Our algorithms and results have immediate policy implications and directly apply to a variety of scenarios, such as where hiring positions or scarce medical resources need to be allocated while taking into account diversity concerns or ethical principles.CCS Concepts: • Theory of computation → Algorithmic game theory and mechanism design; Solution concepts in game theory; • Applied computing → Economics.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-Rank Smart Reserves

Aziz

Sun

2021

Proceedings of the 22nd ACM Conference on Economics and Computation

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“…In other words, the set of displayed reviews should work well together in providing an accurate overview of the product category. There are alternative fair group formation algorithms [6,11,24,27,31,32], but these do not focus on diversity of experience, which has long been of interest in sociology, psychology, and business management [18,20,21,26].…”

Section: Fair Clustering and Group Formationmentioning

confidence: 99%

Hypergraph Clustering for Finding Diverse and Experienced Groups

Amburg,

Veldt,

Benson

2020

Preprint

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The ability for machine learning to exacerbate bias has led to many algorithms centered on fairness. For example, fair clustering algorithms typically focus on balanced representation of protected attributes within clusters. Here, we develop a fair clustering variant where the input data is a hypergraph with multiple edge types, representing information about past experiences of groups of individuals. Our method is based on diversity of experience, instead of protected attributes, with a goal of forming groups that have both experience and diversity with respect to participation in edge types. We model this goal with a regularized edge-based clustering objective, design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions. We demonstrate a potential application of this framework in online review platforms, where the goal is to curate sets of user reviews for a product type. In this context, "experience" corresponds to users familiar with the type of product, and "diversity" to users that have reviewed related products.

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“…Independently, [4] introduce a very similar model where they present approximation algorithms for submodular scores. The work of [1] considers the question of how to elect a committee if the constraints cannot be satisfied, and presents an algorithm for finding an ordinally-optimal committee that comes the closest to satisfying the constraints. Also related is the work on apportionment of [13] which considers how to apportion seats with an arbitrary number of diversity constraints.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper we have interpreted this approach as optimising the ordinal ranking that is the outcome of the cardinal scores of a scoring rule, but scoring rules are just one of many voting procedures that can be used to elect a committee. A purely ordinal model is used by [1], but it is a very specific one -we are given a global preference order over the candidate and have to choose an optimal committee accordingly. This could be interpreted as optimising a best-k rule in the sense of [5].…”

Section: Future Directionsmentioning

confidence: 99%

Electing a committee with dominance constraints

Ianovski¹

2019

Preprint

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We consider the problem of electing a committee of k candidates, subject to some constraints as to what this committee is supposed to look like. In our framework, the candidates are given labels as an abstraction of gender, religion, ethnicity, and other attributes, and the election outcome is constrained by interval constraints -of the form "Between 3 and 5 candidates with label X" -and dominance constraints -"At least as many candidates with label X as with label Y". While in general this problem would require us to rethink how we determine which election outcomes are good, in the case of a committee scoring rule this becomes a constrained optimisation problem -simply find a valid committee with the highest score. In the case of weakly separable rules we show the existence of a polynomial time solution in the case of tree-like constraints, and a fixed-parameter tractable algorithm for the general case, which is otherwise NP-hard.

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A Rule for Committee Selection with Soft Diversity Constraints

Cited by 13 publications

References 16 publications

Multi-Rank Smart Reserves

Multi-Rank Smart Reserves

Hypergraph Clustering for Finding Diverse and Experienced Groups

Electing a committee with dominance constraints

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