Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

• URL: http://arxiv.org/abs/2206.07801v1
• Date: Wed, 15 Jun 2022 20:29:33 GMT
• Title: Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
• Authors: Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, P. Winston Michalak, Shahab Asoodeh, Flavio P. Calmon
• Abstract summary: We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. We provide a parallelizable iterative algorithm for computing the projected classifier and derive both sample complexity and convergence guarantees. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional protected groups, and over 1M samples.
• Score: 8.405162568925405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by post-processing the outputs of the pre-trained classifier by a multiplicative factor. We provide a parallelizable iterative algorithm for computing the projected classifier and derive both sample complexity and convergence guarantees. Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional protected groups, and over 1M samples.

Related papers

• Probabilistic Contrastive Learning for Long-Tailed Visual Recognition [78.70453964041718]
  Longtailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. We propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space.
  arXiv  Detail & Related papers  (2024-03-11T13:44:49Z)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• Multi-Classification using One-versus-One Deep Learning Strategy with Joint Probability Estimates [0.0]
  The proposed model achieves generally higher classification accuracy than other state-of-the-art models. Numerical experiments in different applications show that the proposed model achieves generally higher classification accuracy than other state-of-the-art models.
  arXiv  Detail & Related papers  (2023-06-16T07:54:15Z)
• 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)
• When in Doubt: Improving Classification Performance with Alternating Normalization [57.39356691967766]
  We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution. We empirically demonstrate its effectiveness across a diverse set of classification tasks.
  arXiv  Detail & Related papers  (2021-09-28T02:55:42Z)
• Transductive Few-Shot Learning: Clustering is All You Need? [31.21306826132773]
  We investigate a general formulation for transive few-shot learning, which integrates prototype-based objectives. We find that our method yields competitive performances, in term of accuracy and optimization, while scaling up to large problems. Surprisingly, we find that our general model already achieve competitive performances in comparison to the state-of-the-art learning.
  arXiv  Detail & Related papers  (2021-06-16T16:14:01Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)
• 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)
• Fairness with Overlapping Groups [15.154984899546333]
  A standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this standard fair classification problem using a probabilistic population analysis. Our approach unifies a variety of existing group-fair classification methods and enables extensions to a wide range of non-decomposable multiclass performance metrics and fairness measures.
  arXiv  Detail & Related papers  (2020-06-24T05:01:10Z)
• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective [98.70226503904402]
  Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions. We propose to augment the classic class-balanced learning by explicitly estimating the differences between the class-conditioned distributions with a meta-learning approach.
  arXiv  Detail & Related papers  (2020-03-24T11:28:42Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)

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.