Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models Promptly

• URL: http://arxiv.org/abs/2405.09251v1
• Date: Wed, 15 May 2024 11:07:40 GMT
• Title: Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models Promptly
• Authors: Yijun Bian, Yujie Luo,
• Abstract summary: Existing techniques for assessing the discrimination level of machine learning models include commonly used group and individual fairness measures. We propose a "harmonic fairness measure via manifold (HFM)" based on distances between sets. Empirical results indicate that the proposed fairness measure HFM is valid and that the proposed ApproxDist is effective and efficient.
• Score: 2.002741592555996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly in high-stakes domains. Existing techniques for assessing the discrimination level of ML models include commonly used group and individual fairness measures. However, these two types of fairness measures are usually hard to be compatible with each other, and even two different group fairness measures might be incompatible as well. To address this issue, we investigate to evaluate the discrimination level of classifiers from a manifold perspective and propose a "harmonic fairness measure via manifolds (HFM)" based on distances between sets. Yet the direct calculation of distances might be too expensive to afford, reducing its practical applicability. Therefore, we devise an approximation algorithm named "Approximation of distance between sets (ApproxDist)" to facilitate accurate estimation of distances, and we further demonstrate its algorithmic effectiveness under certain reasonable assumptions. Empirical results indicate that the proposed fairness measure HFM is valid and that the proposed ApproxDist is effective and efficient.

Related papers

• Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes [4.731404257629232]
  We propose a fairness measure based on distances between sets from a manifold perspective. It can deal with a fine-grained discrimination evaluation for several sensitive attributes of multiple values. We also propose two approximation algorithms to accelerate the computation of distances of sets.
  arXiv  Detail & Related papers  (2024-08-12T12:30:48Z)
• On the Maximal Local Disparity of Fairness-Aware Classifiers [35.98015221840018]
  We propose a novel fairness metric called Maximal Cumulative ratio Disparity along varying Predictions' neighborhood (MCDP) To accurately and efficiently calculate the MCDP, we develop a provably exact and an approximate calculation algorithm that greatly reduces the computational complexity with low estimation error.
  arXiv  Detail & Related papers  (2024-06-05T13:35:48Z)
• Learning Fair Classifiers via Min-Max F-divergence Regularization [13.81078324883519]
  We introduce a novel min-max F-divergence regularization framework for learning fair classification models. We show that F-divergence measures possess convexity and differentiability properties. We show that the proposed framework achieves state-of-the-art performance with respect to the trade-off between accuracy and fairness.
  arXiv  Detail & Related papers  (2023-06-28T20:42:04Z)
• Domain Adaptation meets Individual Fairness. And they get along [48.95808607591299]
  We show that algorithmic fairness interventions can help machine learning models overcome distribution shifts. In particular, we show that enforcing suitable notions of individual fairness (IF) can improve the out-of-distribution accuracy of ML models.
  arXiv  Detail & Related papers  (2022-05-01T16:19:55Z)
• Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks [7.763173131630868]
  We propose two metrics to quantitatively evaluate the class-wise bias of two models in comparison to one another. By evaluating the performance of these new metrics and by demonstrating their practical application, we show that they can be used to measure fairness as well as bias.
  arXiv  Detail & Related papers  (2021-10-08T22:35:34Z)
• Algorithmic Fairness Verification with Graphical Models [24.8005399877574]
  We propose an efficient fairness verifier, called FVGM, that encodes correlations among features as a Bayesian network. We show that FVGM leads to an accurate and scalable assessment for more diverse families of fairness-enhancing algorithms.
  arXiv  Detail & Related papers  (2021-09-20T12:05:14Z)
• Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning [51.03781020616402]
  Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications. We propose a few-shot fine-grained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class. Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling fine-grained actions.
  arXiv  Detail & Related papers  (2021-08-15T02:21:01Z)
• Scalable Personalised Item Ranking through Parametric Density Estimation [53.44830012414444]
  Learning from implicit feedback is challenging because of the difficult nature of the one-class problem. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. We propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart.
  arXiv  Detail & Related papers  (2021-05-11T03:38:16Z)
• Rethink Maximum Mean Discrepancy for Domain Adaptation [77.2560592127872]
  This paper theoretically proves two essential facts: 1) minimizing the Maximum Mean Discrepancy equals to maximize the source and target intra-class distances respectively but jointly minimize their variance with some implicit weights, so that the feature discriminability degrades. Experiments on several benchmark datasets not only prove the validity of theoretical results but also demonstrate that our approach could perform better than the comparative state-of-art methods substantially.
  arXiv  Detail & Related papers  (2020-07-01T18:25:10Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)
• Discrimination of POVMs with rank-one effects [62.997667081978825]
  This work provides an insight into the problem of discrimination of positive operator valued measures with rank-one effects. We compare two possible discrimination schemes: the parallel and adaptive ones. We provide an explicit algorithm which allows us to find this adaptive scheme.
  arXiv  Detail & Related papers  (2020-02-13T11:34:50Z)

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.