Fairness-aware Class Imbalanced Learning

• URL: http://arxiv.org/abs/2109.10444v1
• Date: Tue, 21 Sep 2021 22:16:30 GMT
• Title: Fairness-aware Class Imbalanced Learning
• Authors: Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin, Trevor Cohn, Lea Frermann
• Abstract summary: We evaluate long-tail learning methods for tweet sentiment and occupation classification. We extend a margin-loss based approach with methods to enforce fairness.
• Score: 57.45784950421179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.

Related papers

• Learning Confidence Bounds for Classification with Imbalanced Data [42.690254618937196]
  We propose a novel framework that leverages learning theory and concentration inequalities to overcome the shortcomings of traditional solutions. Our method can effectively adapt to the varying degrees of imbalance across different classes, resulting in more robust and reliable classification outcomes.
  arXiv  Detail & Related papers  (2024-07-16T16:02:27Z)
• DAFA: Distance-Aware Fair Adversarial Training [34.94780532071229]
  Under adversarial attacks, the majority of the model's predictions for samples from the worst class are biased towards classes similar to the worst class. We introduce the Distance-Aware Fair Adversarial training (DAFA) methodology, which addresses robust fairness by taking into account the similarities between classes.
  arXiv  Detail & Related papers  (2024-01-23T07:15:47Z)
• Exploring Vacant Classes in Label-Skewed Federated Learning [113.65301899666645]
  Label skews, characterized by disparities in local label distribution across clients, pose a significant challenge in federated learning. This paper introduces FedVLS, a novel approach to label-skewed federated learning that integrates vacant-class distillation and logit suppression simultaneously.
  arXiv  Detail & Related papers  (2024-01-04T16:06:31Z)
• Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP [64.45845091719002]
  Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning.
  arXiv  Detail & Related papers  (2023-02-11T14:54:00Z)
• A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing [68.37496795076203]
  We provide guidance for NLP researchers and practitioners dealing with imbalanced data. We first discuss various types of controlled and real-world class imbalance. We organize the methods by whether they are based on sampling, data augmentation, choice of loss function, staged learning, or model design.
  arXiv  Detail & Related papers  (2022-10-10T13:26:40Z)
• Towards Equal Opportunity Fairness through Adversarial Learning [64.45845091719002]
  Adversarial training is a common approach for bias mitigation in natural language processing. We propose an augmented discriminator for adversarial training, which takes the target class as input to create richer features.
  arXiv  Detail & Related papers  (2022-03-12T02:22:58Z)
• Relieving Long-tailed Instance Segmentation via Pairwise Class Balance [85.53585498649252]
  Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. It causes severe biases of the head classes (with majority samples) against the tailed ones. We propose a novel Pairwise Class Balance (PCB) method, built upon a confusion matrix which is updated during training to accumulate the ongoing prediction preferences.
  arXiv  Detail & Related papers  (2022-01-08T07:48:36Z)
• Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks [7.763173131630868]
  We propose two metrics to quantitatively evaluate the class-wise bias of two models in comparison to one another. By evaluating the performance of these new metrics and by demonstrating their practical application, we show that they can be used to measure fairness as well as bias.
  arXiv  Detail & Related papers  (2021-10-08T22:35:34Z)
• A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set [5.277804553312449]
  We show the importance of understanding how a bias can be introduced into automatic decisions. We first present a mathematical framework for the fair learning problem, specifically in the binary classification setting. We then propose to quantify the presence of bias by using the standard Disparate Impact index on the real and well-known Adult income data set.
  arXiv  Detail & Related papers  (2020-03-31T14:48:36Z)
• Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? [11.435833538081557]
  Empirical Risk Minimization (ERM) may produce a classifier that not only is biased but also has suboptimal accuracy on the true data distribution. We examine the ability of fairness-constrained ERM to correct this problem. We also consider other recovery methods including reweighting the training data, Equalized Odds, and Demographic Parity.
  arXiv  Detail & Related papers  (2019-12-02T22:00:14Z)

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.