Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks

• URL: http://arxiv.org/abs/2110.04397v1
• Date: Fri, 8 Oct 2021 22:35:34 GMT
• Title: Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
• Authors: Cody Blakeney, Gentry Atkinson, Nathaniel Huish, Yan Yan, Vangelis Metris, Ziliang Zong
• Abstract summary: 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.
• Score: 7.763173131630868
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discrimination and can be more difficult to detect and mitigate. A clear gap exists in the current literature on evaluating the relative bias in the performance of multi-class classifiers. In this work, we propose two simple yet effective metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), 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. These demonstrations show that our metrics can address specific needs for measuring bias in multi-class classification.

Related papers

• Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics [47.762333925222926]
  We present a novel metric to quantify biased behaviors of machine learning models. We focus on and apply it to the operational evaluation of face recognition systems.
  arXiv  Detail & Related papers  (2024-09-03T14:19:38Z)
• Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models Promptly [2.002741592555996]
  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.
  arXiv  Detail & Related papers  (2024-05-15T11:07:40Z)
• Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP [64.45845091719002]
  Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning.
  arXiv  Detail & Related papers  (2023-02-11T14:54:00Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Ensembling over Classifiers: a Bias-Variance Perspective [13.006468721874372]
  We build upon the extension to the bias-variance decomposition by Pfau (2013) in order to gain crucial insights into the behavior of ensembles of classifiers. We show that conditional estimates necessarily incur an irreducible error. Empirically, standard ensembling reducesthe bias, leading us to hypothesize that ensembles of classifiers may perform well in part because of this unexpected reduction.
  arXiv  Detail & Related papers  (2022-06-21T17:46:35Z)
• The SAME score: Improved cosine based bias score for word embeddings [49.75878234192369]
  We introduce SAME, a novel bias score for semantic bias in embeddings. We show that SAME is capable of measuring semantic bias and identify potential causes for social bias in downstream tasks.
  arXiv  Detail & Related papers  (2022-03-28T09:28:13Z)
• Information-Theoretic Bias Reduction via Causal View of Spurious Correlation [71.9123886505321]
  We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation. We present a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss. The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios.
  arXiv  Detail & Related papers  (2022-01-10T01:19:31Z)
• Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition [18.07966649678408]
  We propose an information-theoretic, independent bias assessment metric to identify degree of bias against protected demographic attributes. Our metric differs from other methods that rely on classification accuracy or examine the differences between ground truth and predicted labels of protected attributes predicted using a shallow network.
  arXiv  Detail & Related papers  (2021-11-08T17:41:17Z)
• Fairness-aware Class Imbalanced Learning [57.45784950421179]
  We evaluate long-tail learning methods for tweet sentiment and occupation classification. We extend a margin-loss based approach with methods to enforce fairness.
  arXiv  Detail & Related papers  (2021-09-21T22:16:30Z)

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.