FR-Train: A Mutual Information-Based Approach to Fair and Robust Training

• URL: http://arxiv.org/abs/2002.10234v2
• Date: Fri, 3 Jul 2020 07:46:37 GMT
• Title: FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
• Authors: Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh
• Abstract summary: We propose FR-Train, which holistically performs fair and robust model training. In our experiments, FR-Train shows almost no decrease in fairness and accuracy in the presence of data poisoning.
• Score: 33.385118640843416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of data bias and poisoning. However, the existing model fairness techniques mistakenly view poisoned data as an additional bias to be fixed, resulting in severe performance degradation. To address this problem, we propose FR-Train, which holistically performs fair and robust model training. We provide a mutual information-based interpretation of an existing adversarial training-based fairness-only method, and apply this idea to architect an additional discriminator that can identify poisoned data using a clean validation set and reduce its influence. In our experiments, FR-Train shows almost no decrease in fairness and accuracy in the presence of data poisoning by both mitigating the bias and defending against poisoning. We also demonstrate how to construct clean validation sets using crowdsourcing, and release new benchmark datasets.

Related papers

• Achievable Fairness on Your Data With Utility Guarantees [16.78730663293352]
  In machine learning fairness, training models that minimize disparity across different sensitive groups often leads to diminished accuracy. We present a computationally efficient approach to approximate the fairness-accuracy trade-off curve tailored to individual datasets. We introduce a novel methodology for quantifying uncertainty in our estimates, thereby providing practitioners with a robust framework for auditing model fairness.
  arXiv  Detail & Related papers  (2024-02-27T00:59:32Z)
• Towards Poisoning Fair Representations [26.47681999979761]
  This work proposes the first data poisoning framework attacking fair representation learning methods. We induce the model to output unfair representations that contain as much demographic information as possible by injecting carefully crafted poisoning samples into the training data. Experiments on benchmark fairness datasets and state-of-the-art fair representation learning models demonstrate the superiority of our attack.
  arXiv  Detail & Related papers  (2023-09-28T14:51:20Z)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Provable Fairness for Neural Network Models using Formal Verification [10.90121002896312]
  We propose techniques to emphprove fairness using recently developed formal methods that verify properties of neural network models. We show that through proper training, we can reduce unfairness by an average of 65.4% at a cost of less than 1% in AUC score.
  arXiv  Detail & Related papers  (2022-12-16T16:54:37Z)
• Certified Robustness in Federated Learning [54.03574895808258]
  We study the interplay between federated training, personalization, and certified robustness. We find that the simple federated averaging technique is effective in building not only more accurate, but also more certifiably-robust models.
  arXiv  Detail & Related papers  (2022-06-06T12:10:53Z)
• FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes [51.02407217197623]
  We propose a two-stage training algorithm named FAIRIF. It minimizes the loss over the reweighted data set where the sample weights are computed. We show that FAIRIF yields models with better fairness-utility trade-offs against various types of bias.
  arXiv  Detail & Related papers  (2022-01-15T05:14:48Z)
• To be Robust or to be Fair: Towards Fairness in Adversarial Training [83.42241071662897]
  We find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data. We propose a Fair-Robust-Learning (FRL) framework to mitigate this unfairness problem when doing adversarial defenses.
  arXiv  Detail & Related papers  (2020-10-13T02:21:54Z)
• Fairness-aware Agnostic Federated Learning [47.26747955026486]
  We develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution. We use kernel reweighing functions to assign a reweighing value on each training sample in both loss function and fairness constraint. Built model can be directly applied to local sites as it guarantees fairness on local data distributions.
  arXiv  Detail & Related papers  (2020-10-10T17:58:20Z)
• Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination [53.3082498402884]
  A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. We present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning.
  arXiv  Detail & Related papers  (2020-09-25T05:48:56Z)
• On Adversarial Bias and the Robustness of Fair Machine Learning [11.584571002297217]
  We show that giving the same importance to groups of different sizes and distributions, to counteract the effect of bias in training data, can be in conflict with robustness. An adversary who can control sampling or labeling for a fraction of training data, can reduce the test accuracy significantly beyond what he can achieve on unconstrained models. We analyze the robustness of fair machine learning through an empirical evaluation of attacks on multiple algorithms and benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-15T18:17:44Z)
• Precise Tradeoffs in Adversarial Training for Linear Regression [55.764306209771405]
  We provide a precise and comprehensive understanding of the role of adversarial training in the context of linear regression with Gaussian features. We precisely characterize the standard/robust accuracy and the corresponding tradeoff achieved by a contemporary mini-max adversarial training approach. Our theory for adversarial training algorithms also facilitates the rigorous study of how a variety of factors (size and quality of training data, model overparametrization etc.) affect the tradeoff between these two competing accuracies.
  arXiv  Detail & Related papers  (2020-02-24T19:01:47Z)

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.