Enhancing Model Fairness and Accuracy with Similarity Networks: A Methodological Approach

• URL: http://arxiv.org/abs/2411.05648v1
• Date: Fri, 08 Nov 2024 15:43:01 GMT
• Title: Enhancing Model Fairness and Accuracy with Similarity Networks: A Methodological Approach
• Authors: Samira Maghool, Paolo Ceravolo,
• Abstract summary: We use different techniques to map instances into a similarity feature space. Our method's ability to adjust the resolution of pairwise similarity provides clear insights into the relationship between the dataset classification complexity and model fairness.
• Score: 0.20718016474717196
• License:
• Abstract: In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we use different techniques to map instances into a similarity feature space. Our method's ability to adjust the resolution of pairwise similarity provides clear insights into the relationship between the dataset classification complexity and model fairness. Experimental results confirm the promising applicability of the similarity network in promoting fair models. Moreover, leveraging our methodology not only seems promising in providing a fair downstream task such as classification, it also performs well in imputation and augmentation of the dataset satisfying the fairness criteria such as demographic parity and imbalanced classes.

Related papers

• Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning [50.382793324572845]
  Distributed computing involves communication between devices, which requires solving two key problems: efficiency and privacy. In this paper, we analyze a new method that incorporates the ideas of using data similarity and clients sampling. To address privacy concerns, we apply the technique of additional noise and analyze its impact on the convergence of the proposed method.
  arXiv  Detail & Related papers  (2024-09-22T00:49:10Z)
• Enhancing Fairness in Neural Networks Using FairVIC [0.0]
  Mitigating bias in automated decision-making systems, specifically deep learning models, is a critical challenge in achieving fairness. We introduce FairVIC, an innovative approach designed to enhance fairness in neural networks by addressing inherent biases at the training stage. We observe a significant improvement in fairness across all metrics tested, without compromising the model's accuracy to a detrimental extent.
  arXiv  Detail & Related papers  (2024-04-28T10:10:21Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Values [6.752538702870792]
  We propose FairShap, a novel instance-level data re-weighting method for fair algorithmic decision-making. We empirically validate FairShap on several state-of-the-art datasets of different nature. We show how it yields fairer models with similar levels of accuracy than the baselines.
  arXiv  Detail & Related papers  (2023-03-03T13:53:36Z)
• Group-Aware Threshold Adaptation for Fair Classification [9.496524884855557]
  We introduce a novel post-processing method to optimize over multiple fairness constraints. Our method theoretically enables a better upper bound in near optimality than existing method under same condition. Experimental results demonstrate that our method outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-08T04:36:37Z)
• Contrastive Learning for Fair Representations [50.95604482330149]
  Trained classification models can unintentionally lead to biased representations and predictions. Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and difficult to optimise. We propose a method for mitigating bias by incorporating contrastive learning, in which instances sharing the same class label are encouraged to have similar representations.
  arXiv  Detail & Related papers  (2021-09-22T10:47:51Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Robust Fairness under Covariate Shift [11.151913007808927]
  Making predictions that are fair with regard to protected group membership has become an important requirement for classification algorithms. We propose an approach that obtains the predictor that is robust to the worst-case in terms of target performance.
  arXiv  Detail & Related papers  (2020-10-11T04:42:01Z)
• Beyond Individual and Group Fairness [90.4666341812857]
  We present a new data-driven model of fairness that is guided by the unfairness complaints received by the system. Our model supports multiple fairness criteria and takes into account their potential incompatibilities.
  arXiv  Detail & Related papers  (2020-08-21T14:14:44Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)
• Fairness by Explicability and Adversarial SHAP Learning [0.0]
  We propose a new definition of fairness that emphasises the role of an external auditor and model explicability. We develop a framework for mitigating model bias using regularizations constructed from the SHAP values of an adversarial surrogate model. We demonstrate our approaches using gradient and adaptive boosting on: a synthetic dataset, the UCI Adult (Census) dataset and a real-world credit scoring dataset.
  arXiv  Detail & Related papers  (2020-03-11T14:36:34Z)

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.