A Maximal Correlation Approach to Imposing Fairness in Machine Learning

• URL: http://arxiv.org/abs/2012.15259v1
• Date: Wed, 30 Dec 2020 18:15:05 GMT
• Title: A Maximal Correlation Approach to Imposing Fairness in Machine Learning
• Authors: Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory Wornell, Leonid Karlinsky, Rogerio Feris
• Abstract summary: We explore the problem of algorithmic fairness, taking an information-theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria. We show that these algorithms provide smooth performance-fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes)
• Score: 25.773384159810234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information-theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables which are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance-fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).

Related papers

• Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics [4.662958544712181]
  This paper introduces two formal frameworks to tackle open questions in machine learning fairness. In one framework, operator-valued optimisation and min-max objectives are employed to address unfairness in time-series problems. In the second framework, the challenge of lacking sensitive attributes, such as gender and race, in commonly used datasets is addressed.
  arXiv  Detail & Related papers  (2024-11-13T22:29:23Z)
• A Semidefinite Relaxation Approach for Fair Graph Clustering [1.03590082373586]
  This study introduces fair graph clustering within the framework of the disparate impact doctrine. We employ a semidefinite relaxation approach to approximate the underlying optimization problem.
  arXiv  Detail & Related papers  (2024-10-19T22:51:24Z)
• Optimal Group Fair Classifiers from Linear Post-Processing [10.615965454674901]
  We propose a post-processing algorithm for fair classification that mitigates model bias under a unified family of group fairness criteria. It achieves fairness by re-calibrating the output score of the given base model with a "fairness cost" -- a linear combination of the (predicted) group memberships.
  arXiv  Detail & Related papers  (2024-05-07T05:58:44Z)
• 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)
• 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)
• FAIRLEARN:Configurable and Interpretable Algorithmic Fairness [1.2183405753834557]
  There is a need to mitigate any bias arising from either training samples or implicit assumptions made about the data samples. Many approaches have been proposed to make learning algorithms fair by detecting and mitigating bias in different stages of optimization. We propose the FAIRLEARN procedure that produces a fair algorithm by incorporating user constraints into the optimization procedure.
  arXiv  Detail & Related papers  (2021-11-17T03:07:18Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• 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)
• Spectrum-Guided Adversarial Disparity Learning [52.293230153385124]
  We propose a novel end-to-end knowledge directed adversarial learning framework. It portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art.
  arXiv  Detail & Related papers  (2020-07-14T05:46:27Z)
• Fair Correlation Clustering [92.15492066925977]
  We obtain approximation algorithms for correlation clustering under several important types of fairness constraints. We show that fair solutions to correlation clustering can be obtained with limited increase in cost compared to the state-of-the-art (unfair) algorithms.
  arXiv  Detail & Related papers  (2020-02-06T14:28:21Z)

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.