Gradient Based Activations for Accurate Bias-Free Learning

• URL: http://arxiv.org/abs/2202.10943v1
• Date: Thu, 17 Feb 2022 00:30:40 GMT
• Title: Gradient Based Activations for Accurate Bias-Free Learning
• Authors: Vinod K Kurmi, Rishabh Sharma, Yash Vardhan Sharma, Vinay P. Namboodiri
• Abstract summary: We show that a biased discriminator can actually be used to improve this bias-accuracy tradeoff. Specifically, this is achieved by using a feature masking approach using the discriminator's gradients. We show that this simple approach works well to reduce bias as well as improve accuracy significantly.
• Score: 22.264226961225003
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as gender, age or race in question. This discriminator is used adversarially to ensure that it cannot distinguish the bias attributes. The main drawback in such a model is that it directly introduces a trade-off with accuracy as the features that the discriminator deems to be sensitive for discrimination of bias could be correlated with classification. In this work we solve the problem. We show that a biased discriminator can actually be used to improve this bias-accuracy tradeoff. Specifically, this is achieved by using a feature masking approach using the discriminator's gradients. We ensure that the features favoured for the bias discrimination are de-emphasized and the unbiased features are enhanced during classification. We show that this simple approach works well to reduce bias as well as improve accuracy significantly. We evaluate the proposed model on standard benchmarks. We improve the accuracy of the adversarial methods while maintaining or even improving the unbiasness and also outperform several other recent methods.

Related papers

• Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization [13.773597081543185]
  We introduce a novel debiasing regularization technique based on the class-wise variance of embeddings. Our method does not require attribute labels and targets any attribute, thus addressing the shortcomings of existing debiasing methods.
  arXiv  Detail & Related papers  (2024-09-29T03:56:50Z)
• Language-guided Detection and Mitigation of Unknown Dataset Bias [23.299264313976213]
  We propose a framework to identify potential biases as keywords without prior knowledge based on the partial occurrence in the captions. Our framework not only outperforms existing methods without prior knowledge, but also is even comparable with a method that assumes prior knowledge.
  arXiv  Detail & Related papers  (2024-06-05T03:11:33Z)
• Classes Are Not Equal: An Empirical Study on Image Recognition Fairness [100.36114135663836]
  We experimentally demonstrate that classes are not equal and the fairness issue is prevalent for image classification models across various datasets. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. Data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification.
  arXiv  Detail & Related papers  (2024-02-28T07:54:50Z)
• Mitigating Label Bias in Machine Learning: Fairness through Confident Learning [22.031325797588476]
  Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias. In this paper, we demonstrate that it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning.
  arXiv  Detail & Related papers  (2023-12-14T08:55:38Z)
• Shedding light on underrepresentation and Sampling Bias in machine learning [0.0]
  We show how discrimination can be decomposed into variance, bias, and noise. We challenge the commonly accepted mitigation approach that discrimination can be addressed by collecting more samples of the underrepresented group.
  arXiv  Detail & Related papers  (2023-06-08T09:34:20Z)
• A Differentiable Distance Approximation for Fairer Image Classification [31.471917430653626]
  We propose a differentiable approximation of the variance of demographics, a metric that can be used to measure the bias, or unfairness, in an AI model. Our approximation can be optimised alongside the regular training objective which eliminates the need for any extra models during training. We demonstrate that our approach improves the fairness of AI models in varied task and dataset scenarios.
  arXiv  Detail & Related papers  (2022-10-09T23:02:18Z)
• Optimising Equal Opportunity Fairness in Model Training [60.0947291284978]
  Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. We propose two novel training objectives which directly optimise for the widely-used criterion of it equal opportunity, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.
  arXiv  Detail & Related papers  (2022-05-05T01:57:58Z)
• Towards Equal Opportunity Fairness through Adversarial Learning [64.45845091719002]
  Adversarial training is a common approach for bias mitigation in natural language processing. We propose an augmented discriminator for adversarial training, which takes the target class as input to create richer features.
  arXiv  Detail & Related papers  (2022-03-12T02:22:58Z)
• 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)
• 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)
• Balancing out Bias: Achieving Fairness Through Training Reweighting [58.201275105195485]
  Bias in natural language processing arises from models learning characteristics of the author such as gender and race. Existing methods for mitigating and measuring bias do not directly account for correlations between author demographics and linguistic variables. This paper introduces a very simple but highly effective method for countering bias using instance reweighting.
  arXiv  Detail & Related papers  (2021-09-16T23:40:28Z)

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.