How Gender Interacts with Political Values: A Case Study on Czech BERT Models
- URL: http://arxiv.org/abs/2403.13514v1
- Date: Wed, 20 Mar 2024 11:30:45 GMT
- Title: How Gender Interacts with Political Values: A Case Study on Czech BERT Models
- Authors: Adnan Al Ali, Jindřich Libovický,
- Abstract summary: This case study focuses on the political biases of pre-trained encoders in Czech.
Because Czech is a gendered language, we measure how the grammatical gender coincides with responses to men and women in the survey.
We find that the models do not assign statement probability following value-driven reasoning.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neural language models, which reach state-of-the-art results on most natural language processing tasks, are trained on large text corpora that inevitably contain value-burdened content and often capture undesirable biases, which the models reflect. This case study focuses on the political biases of pre-trained encoders in Czech and compares them with a representative value survey. Because Czech is a gendered language, we also measure how the grammatical gender coincides with responses to men and women in the survey. We introduce a novel method for measuring the model's perceived political values. We find that the models do not assign statement probability following value-driven reasoning, and there is no systematic difference between feminine and masculine sentences. We conclude that BERT-sized models do not manifest systematic alignment with political values and that the biases observed in the models are rather due to superficial imitation of training data patterns than systematic value beliefs encoded in the models.
Related papers
- Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
Existing machine translation gender bias evaluations are primarily focused on male and female genders.
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words)
We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
arXiv Detail & Related papers (2024-07-23T08:13:51Z) - Are Models Biased on Text without Gender-related Language? [14.931375031931386]
We introduce UnStereoEval (USE), a novel framework for investigating gender bias in stereotype-free scenarios.
USE defines a sentence-level score based on pretraining data statistics to determine if the sentence contain minimal word-gender associations.
We find low fairness across all 28 tested models, suggesting that bias does not solely stem from the presence of gender-related words.
arXiv Detail & Related papers (2024-05-01T15:51:15Z) - Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender
Perturbation over Fairytale Texts [87.62403265382734]
Recent studies show that traditional fairytales are rife with harmful gender biases.
This work aims to assess learned biases of language models by evaluating their robustness against gender perturbations.
arXiv Detail & Related papers (2023-10-16T22:25:09Z) - VisoGender: A dataset for benchmarking gender bias in image-text pronoun
resolution [80.57383975987676]
VisoGender is a novel dataset for benchmarking gender bias in vision-language models.
We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas.
We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
arXiv Detail & Related papers (2023-06-21T17:59:51Z) - Gender Biases in Automatic Evaluation Metrics for Image Captioning [87.15170977240643]
We conduct a systematic study of gender biases in model-based evaluation metrics for image captioning tasks.
We demonstrate the negative consequences of using these biased metrics, including the inability to differentiate between biased and unbiased generations.
We present a simple and effective way to mitigate the metric bias without hurting the correlations with human judgments.
arXiv Detail & Related papers (2023-05-24T04:27:40Z) - Measuring Gender Bias in West Slavic Language Models [41.49834421110596]
We introduce the first template-based dataset in Czech, Polish, and Slovak for measuring gender bias towards male, female and non-binary subjects.
We measure gender bias encoded in West Slavic language models by quantifying the toxicity and genderness of the generated words.
We find that these language models produce hurtful completions that depend on the subject's gender.
arXiv Detail & Related papers (2023-04-12T11:49:43Z) - Efficient Gender Debiasing of Pre-trained Indic Language Models [0.0]
The gender bias present in the data on which language models are pre-trained gets reflected in the systems that use these models.
In our paper, we measure gender bias associated with occupations in Hindi language models.
Our results reflect that the bias is reduced post-introduction of our proposed mitigation techniques.
arXiv Detail & Related papers (2022-09-08T09:15:58Z) - The Birth of Bias: A case study on the evolution of gender bias in an
English language model [1.6344851071810076]
We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus.
We find that the representation of gender is dynamic and identify different phases during training.
We show that gender information is represented increasingly locally in the input embeddings of the model.
arXiv Detail & Related papers (2022-07-21T00:59:04Z) - Quantifying Gender Bias Towards Politicians in Cross-Lingual Language
Models [104.41668491794974]
We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender.
We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
arXiv Detail & Related papers (2021-04-15T15:03:26Z) - Multi-Dimensional Gender Bias Classification [67.65551687580552]
Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text.
We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
arXiv Detail & Related papers (2020-05-01T21:23:20Z)
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.