Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach

• URL: http://arxiv.org/abs/2405.01063v2
• Date: Mon, 27 May 2024 07:33:45 GMT
• Title: Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
• Authors: Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He,
• Abstract summary: We propose Distributionally Robust Fair Optimization (DRFO) to ensure fairness in recommender systems. DRFO minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes. We provide theoretical and empirical evidence to demonstrate that our method can effectively ensure fairness in recommender systems.
• Score: 46.61096160935783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes becomes an imperative requirement. Prior approaches for enhancing fairness in recommender systems presume the availability of all sensitive attributes, which can be difficult to obtain due to privacy concerns or inadequate means of capturing these attributes. In practice, the efficacy of these approaches is limited, pushing us to investigate ways of promoting fairness with limited sensitive attribute information. Toward this goal, it is important to reconstruct missing sensitive attributes. Nevertheless, reconstruction errors are inevitable due to the complexity of real-world sensitive attribute reconstruction problems and legal regulations. Thus, we pursue fair learning methods that are robust to reconstruction errors. To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors. We provide theoretical and empirical evidence to demonstrate that our method can effectively ensure fairness in recommender systems when only limited sensitive attributes are accessible.

Related papers

• Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning [49.417414031031264]
  This paper studies learning fair encoders in a self-supervised learning setting. All data are unlabeled and only a small portion of them are annotated with sensitive attributes.
  arXiv  Detail & Related papers  (2024-06-09T08:11:12Z)
• CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System [16.84754752395103]
  This work takes a critical stance on previous studies concerning fairness evaluation in Large Language Model (LLM)-based recommender systems. We introduce CFaiRLLM, an enhanced evaluation framework that not only incorporates true preference alignment but also rigorously examines intersectional fairness.
  arXiv  Detail & Related papers  (2024-03-08T20:44:59Z)
• A Sequentially Fair Mechanism for Multiple Sensitive Attributes [0.46040036610482665]
  In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. We propose a sequential framework, which allows to progressively achieve fairness across a set of sensitive features. Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness.
  arXiv  Detail & Related papers  (2023-09-12T22:31:57Z)
• A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition [10.821982414387525]
  We show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others.
  arXiv  Detail & Related papers  (2023-07-28T01:34:55Z)
• 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)
• Group Fairness with Uncertainty in Sensitive Attributes [34.608332397776245]
  A fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. We propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes. Our algorithm is applicable to both discrete and continuous sensitive attributes and is effective in real-world classification and regression tasks.
  arXiv  Detail & Related papers  (2023-02-16T04:33:00Z)
• Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes [70.6326967720747]
  It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. We introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. We empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.
  arXiv  Detail & Related papers  (2022-11-11T11:28:46Z)
• Fairness via Adversarial Attribute Neighbourhood Robust Learning [49.93775302674591]
  We propose a principled underlineRobust underlineAdversarial underlineAttribute underlineNeighbourhood (RAAN) loss to debias the classification head.
  arXiv  Detail & Related papers  (2022-10-12T23:39:28Z)
• Mitigating Algorithmic Bias with Limited Annotations [65.060639928772]
  When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mitigate bias. We propose Active Penalization Of Discrimination (APOD), an interactive framework to guide the limited annotations towards maximally eliminating the effect of algorithmic bias. APOD shows comparable performance to fully annotated bias mitigation, which demonstrates that APOD could benefit real-world applications when sensitive information is limited.
  arXiv  Detail & Related papers  (2022-07-20T16:31:19Z)
• Fairness without the sensitive attribute via Causal Variational Autoencoder [17.675997789073907]
  Due to privacy purposes and var-ious regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. By leveraging recent developments for approximate inference, we propose an approach to fill this gap. Based on a causal graph, we rely on a new variational auto-encoding based framework named SRCVAE to infer a sensitive information proxy.
  arXiv  Detail & Related papers  (2021-09-10T17:12:52Z)

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.