Less can be more: representational vs. stereotypical gender bias in facial expression recognition

• URL: http://arxiv.org/abs/2406.17405v1
• Date: Tue, 25 Jun 2024 09:26:49 GMT
• Title: Less can be more: representational vs. stereotypical gender bias in facial expression recognition
• Authors: Iris Dominguez-Catena, Daniel Paternain, Aranzazu Jurio, Mikel Galar,
• Abstract summary: Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This paper investigates the propagation of demographic biases from datasets into machine learning models. We focus on the gender demographic component, analyzing two types of bias: representational and stereotypical.
• Score: 3.9698529891342207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This is particularly concerning with the increasing use of large, unsupervised datasets for training foundational models. Traditionally, demographic biases within these datasets have not been well-understood, limiting our ability to understand how they propagate to the models themselves. To address this issue, this paper investigates the propagation of demographic biases from datasets into machine learning models. We focus on the gender demographic component, analyzing two types of bias: representational and stereotypical. For our analysis, we consider the domain of facial expression recognition (FER), a field known to exhibit biases in most popular datasets. We use Affectnet, one of the largest FER datasets, as our baseline for carefully designing and generating subsets that incorporate varying strengths of both representational and stereotypical bias. Subsequently, we train several models on these biased subsets, evaluating their performance on a common test set to assess the propagation of bias into the models' predictions. Our results show that representational bias has a weaker impact than expected. Models exhibit a good generalization ability even in the absence of one gender in the training dataset. Conversely, stereotypical bias has a significantly stronger impact, primarily concentrated on the biased class, although it can also influence predictions for unbiased classes. These results highlight the need for a bias analysis that differentiates between types of bias, which is crucial for the development of effective bias mitigation strategies.

Related papers

• Improving Bias Mitigation through Bias Experts in Natural Language Understanding [10.363406065066538]
  We propose a new debiasing framework that introduces binary classifiers between the auxiliary model and the main model. Our proposed strategy improves the bias identification ability of the auxiliary model.
  arXiv  Detail & Related papers  (2023-12-06T16:15:00Z)
• Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models [23.65626682262062]
  We quantify bias amplification in pretraining and after fine-tuning on three families of vision-and-language models. Overall, we find that bias amplification in pretraining and after fine-tuning are independent.
  arXiv  Detail & Related papers  (2023-10-26T16:19:19Z)
• Fast Model Debias with Machine Unlearning [54.32026474971696]
  Deep neural networks might behave in a biased manner in many real-world scenarios. Existing debiasing methods suffer from high costs in bias labeling or model re-training. We propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases.
  arXiv  Detail & Related papers  (2023-10-19T08:10:57Z)
• Analyzing Bias in Diffusion-based Face Generation Models [75.80072686374564]
  Diffusion models are increasingly popular in synthetic data generation and image editing applications. We investigate the presence of bias in diffusion-based face generation models with respect to attributes such as gender, race, and age. We examine how dataset size affects the attribute composition and perceptual quality of both diffusion and Generative Adversarial Network (GAN) based face generation models.
  arXiv  Detail & Related papers  (2023-05-10T18:22:31Z)
• Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
  We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
  arXiv  Detail & Related papers  (2023-01-31T20:09:33Z)
• Gender Stereotyping Impact in Facial Expression Recognition [1.5340540198612824]
  In recent years, machine learning-based models have become the most popular approach to Facial Expression Recognition (FER) In publicly available FER datasets, apparent gender representation is usually mostly balanced, but their representation in the individual label is not. We generate derivative datasets with different amounts of stereotypical bias by altering the gender proportions of certain labels. We observe a discrepancy in the recognition of certain emotions between genders of up to $29 %$ under the worst bias conditions.
  arXiv  Detail & Related papers  (2022-10-11T10:52:23Z)
• Unsupervised Learning of Unbiased Visual Representations [10.871587311621974]
  Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. We propose a fully unsupervised debiasing framework, consisting of three steps. We employ state-of-the-art supervised debiasing techniques to obtain an unbiased model.
  arXiv  Detail & Related papers  (2022-04-26T10:51:50Z)
• Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification [57.53567756716656]
  We study the problem of developing debiased chest X-ray diagnosis models without knowing exactly the bias labels. We propose a novel algorithm, pseudo bias-balanced learning, which first captures and predicts per-sample bias labels. Our proposed method achieved consistent improvements over other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-18T11:02:18Z)
• 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)
• Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space [1.858151490268935]
  This work targets biased generative models' behaviors, identifying the cause of the biases and eliminating them. We can (as expected) conclude that biased data causes biased predictions of face frontalization models. We found that the seemingly obvious choice of 50:50 proportions was not the best for this dataset to reduce biased behavior on female faces.
  arXiv  Detail & Related papers  (2021-08-23T16:09:18Z)
• UnQovering Stereotyping Biases via Underspecified Questions [68.81749777034409]
  We present UNQOVER, a framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors. We use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion.
  arXiv  Detail & Related papers  (2020-10-06T01:49:52Z)

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.