Learning Fair Models without Sensitive Attributes: A Generative Approach

• URL: http://arxiv.org/abs/2203.16413v2
• Date: Wed, 02 Oct 2024 20:15:09 GMT
• Title: Learning Fair Models without Sensitive Attributes: A Generative Approach
• Authors: Huaisheng Zhu, Enyan Dai, Hui Liu, Suhang Wang,
• Abstract summary: We study a novel problem of learning fair models without sensitive attributes by exploring relevant features. We propose a probabilistic generative framework to effectively estimate the sensitive attribute from the training data. Experimental results on real-world datasets show the effectiveness of our framework.
• Score: 33.196044483534784
• License:
• Abstract: Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues. The lack of sensitive attributes challenges many existing fair classifiers. Though we lack sensitive attributes, for many applications, there usually exists features or information of various formats that are relevant to sensitive attributes. For example, purchase history of a person can reflect his or her race, which would help for learning fair classifiers on race. However, the work on exploring relevant features for learning fair models without sensitive attributes is rather limited. Therefore, in this paper, we study a novel problem of learning fair models without sensitive attributes by exploring relevant features. We propose a probabilistic generative framework to effectively estimate the sensitive attribute from the training data with relevant features in various formats and utilize the estimated sensitive attribute information to learn fair models. Experimental results on real-world datasets show the effectiveness of our framework in terms of both accuracy and fairness.

Related papers

• What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning [52.51430732904994]
  In reinforcement learning problems, agents must consider long-term fairness while maximizing returns. Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. We introduce a novel notion called dynamics fairness, which explicitly captures the inequality stemming from environmental dynamics.
  arXiv  Detail & Related papers  (2024-04-16T22:47:59Z)
• High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning [54.86882315023791]
  We propose an innovative approach called High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning (HDAFL) HDAFL utilizes multiple convolutional kernels to automatically learn discriminative regions highly correlated with attributes in images. We also introduce a Transformer-based attribute discrimination encoder to enhance the discriminative capability among attributes.
  arXiv  Detail & Related papers  (2024-04-07T13:17:47Z)
• Achieve Fairness without Demographics for Dermatological Disease Diagnosis [17.792332189055223]
  We propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training. Inspired by prior work highlighting the impact of feature entanglement on fairness, we enhance the model features by capturing the features related to the sensitive and target attributes. This ensures that the model can only classify based on the features related to the target attribute without relying on features associated with sensitive attributes.
  arXiv  Detail & Related papers  (2024-01-16T02:49:52Z)
• Improving Fairness using Vision-Language Driven Image Augmentation [60.428157003498995]
  Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin color) with unrelated attributes (downstream tasks) This paper proposes a method to mitigate these correlations to improve fairness.
  arXiv  Detail & Related papers  (2023-11-02T19:51:10Z)
• Fairness Under Demographic Scarce Regime [7.523105080786704]
  We propose a framework to build attribute classifiers that achieve better fairness-accuracy tradeoffs. We show that enforcing fairness constraints on samples with uncertain sensitive attributes can negatively impact the fairness-accuracy tradeoff. Our framework can outperform models trained with fairness constraints on the true sensitive attributes in most benchmarks.
  arXiv  Detail & Related papers  (2023-07-24T19:07:34Z)
• Towards Assumption-free Bias Mitigation [47.5131072745805]
  We propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors.
  arXiv  Detail & Related papers  (2023-07-09T05:55:25Z)
• Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach [14.344142985726853]
  We study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain.
  arXiv  Detail & Related papers  (2022-06-08T02:53:18Z)
• Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder [92.67156911466397]
  We propose a semi-supervised fair representation learning approach based on adversarial variational autoencoder. We use a bias-aware model to capture inherent bias information on sensitive attribute. We also use a bias-free model to learn debiased fair representations by using adversarial learning to remove bias information from them.
  arXiv  Detail & Related papers  (2022-04-01T15:57:47Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features [29.94644351343916]
  We propose a novel framework which simultaneously uses these related features for accurate prediction and regularizing the model to be fair. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2021-04-29T17:52:11Z)
• Fairness-Aware Learning with Prejudice Free Representations [2.398608007786179]
  We propose a novel algorithm that can effectively identify and treat latent discriminating features. The approach helps to collect discrimination-free features that would improve the model performance.
  arXiv  Detail & Related papers  (2020-02-26T10:06:31Z)

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.