Optimal Transport of Binary Classifiers to Fairness

• URL: http://arxiv.org/abs/2202.03814v1
• Date: Tue, 8 Feb 2022 12:16:24 GMT
• Title: Optimal Transport of Binary Classifiers to Fairness
• Authors: Maarten Buyl, Tijl De Bie
• Abstract summary: We show that Optimal Transport to Fairness (OTF) can be used to achieve an effective trade-off between predictive power and fairness. Experiments show that OTF can be used to achieve an effective trade-off between predictive power and fairness.
• Score: 16.588468396705366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Much of the past work on fairness in machine learning has focused on forcing the predictions of classifiers to have similar statistical properties for individuals of different demographics. Yet, such methods often simply perform a rescaling of the classifier scores and ignore whether individuals of different groups have similar features. Our proposed method, Optimal Transport to Fairness (OTF), applies Optimal Transport (OT) to take this similarity into account by quantifying unfairness as the smallest cost of OT between a classifier and any score function that satisfies fairness constraints. For a flexible class of linear fairness constraints, we show a practical way to compute OTF as an unfairness cost term that can be added to any standard classification setting. Experiments show that OTF can be used to achieve an effective trade-off between predictive power and fairness.

Related papers

• Distributionally Generative Augmentation for Fair Facial Attribute Classification [69.97710556164698]
  Facial Attribute Classification (FAC) holds substantial promise in widespread applications. FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation.
  arXiv  Detail & Related papers  (2024-03-11T10:50:53Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• Learning Fair Classifiers via Min-Max F-divergence Regularization [13.81078324883519]
  We introduce a novel min-max F-divergence regularization framework for learning fair classification models. We show that F-divergence measures possess convexity and differentiability properties. We show that the proposed framework achieves state-of-the-art performance with respect to the trade-off between accuracy and fairness.
  arXiv  Detail & Related papers  (2023-06-28T20:42:04Z)
• Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes [70.6326967720747]
  It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. We introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. We empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.
  arXiv  Detail & Related papers  (2022-11-11T11:28:46Z)
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts [107.72786199113183]
  We propose a novel fairness learning method termed CUrvature MAtching (CUMA) CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts. We evaluate our method on three popular fairness datasets.
  arXiv  Detail & Related papers  (2022-07-04T02:37:50Z)
• Fair Group-Shared Representations with Normalizing Flows [68.29997072804537]
  We develop a fair representation learning algorithm which is able to map individuals belonging to different groups in a single group. We show experimentally that our methodology is competitive with other fair representation learning algorithms.
  arXiv  Detail & Related papers  (2022-01-17T10:49:49Z)
• Fair Tree Learning [0.15229257192293202]
  Various optimisation criteria combine classification performance with a fairness metric. Current fair decision tree methods only optimise for a fixed threshold on both the classification task as well as the fairness metric. We propose a threshold-independent fairness metric termed uniform demographic parity, and a derived splitting criterion entitled SCAFF -- Splitting Criterion AUC for Fairness.
  arXiv  Detail & Related papers  (2021-10-18T13:40:25Z)
• Fair Normalizing Flows [10.484851004093919]
  We present Fair Normalizing Flows (FNF), a new approach offering more rigorous fairness guarantees for learned representations. The main advantage of FNF is that its exact likelihood computation allows us to obtain guarantees on the maximum unfairness of any potentially adversarial downstream predictor. We experimentally demonstrate the effectiveness of FNF in enforcing various group fairness notions, as well as other attractive properties such as interpretability and transfer learning.
  arXiv  Detail & Related papers  (2021-06-10T17:35:59Z)
• Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm [33.145522561104464]
  The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness notions as regularization terms. We leverage this property to define a differentiable relaxation that approximates fairness notions provably better than existing relaxations.
  arXiv  Detail & Related papers  (2020-09-09T17:40:24Z)
• Ensuring Fairness Beyond the Training Data [22.284777913437182]
  We develop classifiers that are fair with respect to the training distribution and for a class of perturbations. Based on online learning algorithm, we develop an iterative algorithm that converges to a fair and robust solution. Our experiments show that there is an inherent trade-off between fairness and accuracy of such classifiers.
  arXiv  Detail & Related papers  (2020-07-12T16:20:28Z)
• SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness [50.916483212900275]
  We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regularizer efficiently.
  arXiv  Detail & Related papers  (2020-06-25T04:31:57Z)

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.