Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features

• URL: http://arxiv.org/abs/2308.08482v1
• Date: Sun, 13 Aug 2023 00:40:22 GMT
• Title: Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features
• Authors: Yi Zhang, Jitao Sang, Junyang Wang, Dongmei Jiang, Yaowei Wang
• Abstract summary: We propose emphShortcut Debiasing, to first transfer the target task's learning of bias attributes from bias features to shortcut features. We achieve significant improvements over the state-of-the-art debiasing methods in both accuracy and fairness.
• Score: 47.01860331227165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societal applications, such as hiring, banking, and criminal justice. Existing work tackles this issue by minimizing the employed information about social attributes in models for debiasing. However, the high correlation between target task and these social attributes makes learning on the target task incompatible with debiasing. Given that model bias arises due to the learning of bias features (\emph{i.e}., gender) that help target task optimization, we explore the following research question: \emph{Can we leverage shortcut features to replace the role of bias feature in target task optimization for debiasing?} To this end, we propose \emph{Shortcut Debiasing}, to first transfer the target task's learning of bias attributes from bias features to shortcut features, and then employ causal intervention to eliminate shortcut features during inference. The key idea of \emph{Shortcut Debiasing} is to design controllable shortcut features to on one hand replace bias features in contributing to the target task during the training stage, and on the other hand be easily removed by intervention during the inference stage. This guarantees the learning of the target task does not hinder the elimination of bias features. We apply \emph{Shortcut Debiasing} to several benchmark datasets, and achieve significant improvements over the state-of-the-art debiasing methods in both accuracy and fairness.

Related papers

• Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation Model [86.9619638550683]
  Vision-language foundation models have exhibited remarkable success across a multitude of downstream tasks due to their scalability on extensive image-text paired data. However, these models display significant limitations when applied to downstream tasks, such as fine-grained image classification, as a result of decision shortcuts''
  arXiv  Detail & Related papers  (2024-03-01T09:01:53Z)
• Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy [22.69591517487717]
  We show that default-ERM's preference for maximizing the margin leads to models that depend more on the shortcut than the stable feature. We develop loss functions that encourage uniform-margin solutions, called margin control (MARG-CTRL)
  arXiv  Detail & Related papers  (2023-08-24T04:39:25Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations [12.248793742165278]
  We bridge the gap between fairness and explainability by presenting a novel perspective of procedure-oriented fairness based on explanations. We propose a Comprehensive Fairness Algorithm (CFA), which simultaneously fulfills multiple objectives - improving traditional fairness, satisfying explanation fairness, and maintaining the utility performance.
  arXiv  Detail & Related papers  (2022-12-07T18:35:54Z)
• Fair Visual Recognition via Intervention with Proxy Features [13.280828458515062]
  Existing work minimizes information about social attributes in models for debiasing. High correlation between target task and social attributes makes bias mitigation incompatible with target task accuracy. We propose emph Proxy Debiasing, to first transfer the target task's learning of bias information from bias features to artificial proxy features, and then employ causal intervention to eliminate proxy features in inference.
  arXiv  Detail & Related papers  (2022-11-02T16:33:49Z)
• Controlling Bias Exposure for Fair Interpretable Predictions [11.364105288235308]
  We argue that a favorable debiasing method should use sensitive information 'fairly' rather than blindly eliminating it. Our model achieves a desirable trade-off between debiasing and task performance along with producing debiased rationales as evidence.
  arXiv  Detail & Related papers  (2022-10-14T01:49:01Z)
• Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation [44.319739489968164]
  Deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks. In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution. We propose a training strategy Less-Learn-Shortcut (LLS): our strategy quantifies the biased degree of the biased examples and down-weights them accordingly.
  arXiv  Detail & Related papers  (2022-05-25T09:08:35Z)
• Optimising Equal Opportunity Fairness in Model Training [60.0947291284978]
  Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. We propose two novel training objectives which directly optimise for the widely-used criterion of it equal opportunity, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.
  arXiv  Detail & Related papers  (2022-05-05T01:57:58Z)
• Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization [77.8735802150511]
  We propose a cross-sample adversarial debiasing (CSAD) method to remove the bias information misused by the target task. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-08-11T21:17:02Z)
• Learning Stable Classifiers by Transferring Unstable Features [59.06169363181417]
  We study transfer learning in the presence of spurious correlations. We experimentally demonstrate that directly transferring the stable feature extractor learned on the source task may not eliminate these biases for the target task. We hypothesize that the unstable features in the source task and those in the target task are directly related.
  arXiv  Detail & Related papers  (2021-06-15T02:41:12Z)

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.