Fair and Useful Cohort Selection

• URL: http://arxiv.org/abs/2009.02207v2
• Date: Wed, 6 Apr 2022 17:28:43 GMT
• Title: Fair and Useful Cohort Selection
• Authors: Konstantina Bairaktari and Paul Langton and Huy L. Nguyen and Niklas Smedemark-Margulies and Jonathan Ullman
• Abstract summary: Dwork and Ilvento introduce an archetypal problem called fair-cohort-selection problem. A single fair classifier is composed with itself to select a group of candidates of a given size. We give optimal (or approximately optimal)-time algorithms for this problem in both an offline setting and an online setting.
• Score: 12.319543784920304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness. To study fairness under composition, Dwork and Ilvento introduced an archetypal problem called fair-cohort-selection problem, where a single fair classifier is composed with itself to select a group of candidates of a given size, and proposed a solution to this problem. In this work we design algorithms for selecting cohorts that not only preserve fairness, but also maximize the utility of the selected cohort under two notions of utility that we introduce and motivate. We give optimal (or approximately optimal) polynomial-time algorithms for this problem in both an offline setting, and an online setting where candidates arrive one at a time and are classified as they arrive.

Related papers

• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• Fair and Optimal Classification via Post-Processing [10.163721748735801]
  This paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems. We show that the minimum error rate achievable by randomized and attribute-aware fair classifiers is given by the optimal value of a Wasserstein-barycenter problem.
  arXiv  Detail & Related papers  (2022-11-03T00:04:04Z)
• Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency [0.0]
  This paper focuses on the fairness concepts of PPV parity, false omission rate (FOR) parity, and sufficiency. We show that group-specific threshold rules are optimal for PPV parity and FOR parity. We also provide a solution for the optimal decision rules satisfying the fairness constraint sufficiency.
  arXiv  Detail & Related papers  (2022-06-05T18:47:34Z)
• Fair Sequential Selection Using Supervised Learning Models [11.577534539649374]
  We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. We show that even with a pre-trained model that satisfies the common fairness notions, the selection outcomes may still be biased against certain demographic groups. We introduce a new fairness notion, Equal Selection (ES),'' suitable for sequential selection problems and propose a post-processing approach to satisfy the ES fairness notion.
  arXiv  Detail & Related papers  (2021-10-26T19:45:26Z)
• Fair and Optimal Cohort Selection for Linear Utilities [0.0]
  We introduce the fair cohort selection problem, which captures a specific application where a single fair classifier is composed with itself to pick a group of candidates of size exactly $k$. We give approximately optimal-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.
  arXiv  Detail & Related papers  (2021-02-15T17:26:46Z)
• Beyond Individual and Group Fairness [90.4666341812857]
  We present a new data-driven model of fairness that is guided by the unfairness complaints received by the system. Our model supports multiple fairness criteria and takes into account their potential incompatibilities.
  arXiv  Detail & Related papers  (2020-08-21T14:14:44Z)
• Fair Hierarchical Clustering [92.03780518164108]
  We define a notion of fairness that mitigates over-representation in traditional clustering. We show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.
  arXiv  Detail & Related papers  (2020-06-18T01:05:11Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Genetic programming approaches to learning fair classifiers [4.901632310846025]
  We discuss current approaches to fairness and motivate proposals that incorporate fairness into genetic programming for classification. The first is to incorporate a fairness objective into multi-objective optimization. The second is to adapt lexicase selection to define cases dynamically over intersections of protected groups.
  arXiv  Detail & Related papers  (2020-04-28T04:20:25Z)
• Optimal Clustering from Noisy Binary Feedback [75.17453757892152]
  We study the problem of clustering a set of items from binary user feedback. We devise an algorithm with a minimal cluster recovery error rate. For adaptive selection, we develop an algorithm inspired by the derivation of the information-theoretical error lower bounds.
  arXiv  Detail & Related papers  (2019-10-14T09:18:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.