Multi-Dimensional Gender Bias Classification

Proceedings of the First Workshop on Gender Bias in Natural Language Processing, 2019

The purpose of this paper is to present an empirical study on gender bias in text. Current research in this field is focused on detecting and correcting for gender bias in existing machine learning models rather than approaching the issue at the dataset level. The underlying motivation is to create a dataset which could enable machines to learn to differentiate bias writing from non-bias writing. A taxonomy is proposed for structural and contextual gender biases which can manifest themselves in text. A methodology is proposed to fetch one type of structural gender bias, Gender Generalization. We explore the IMDB movie review dataset and 9 different corpora from Project Gutenberg. By filtering out irrelevant sentences, the remaining pool of candidate sentences are sent for human validation. A total of 6123 judgments are made on 1627 sentences and after a quality check on randomly selected sentences we obtain an accuracy of 75%. Out of the 1627 sentences, 808 sentence were labeled as Gender Generalizations. The inter-rater reliability amongst labelers was of 61.14%.

2021

Identifying sexism in social networks is the focus of the EXISTIberLEF 2021 shared task. By participating in this task, the aim of the MultiAzterTest team is to see if linguistically motivated features can help in the detection of sexism. That is why, we present the three approaches: i) an approach based on language models, ii) an approach based on linguistic and stylistic features + machine learning classifiers and iii) an approach combining the previous approaches. The language model approach obtains the best results in the test data. However, the approaches that use linguistic and stylistic features offer more interpretability.

Online Social Networks and Media

Online sexism has become an increasing concern in social media platforms as it has affected the healthy development of the Internet and can have negative effects in society. While research in the sexism detection domain is growing, most of this research focuses on English as the language and on Twitter as the platform. Our objective here is to broaden the scope of this research by considering the Chinese language on Sina Weibo. We propose the first Chinese sexism dataset -Sina Weibo Sexism Review (SWSR) dataset -, as well as a large Chinese lexicon SexHateLex made of abusive and gender-related terms. We introduce our data collection and annotation process, and provide an exploratory analysis of the dataset characteristics to validate its quality and to show how sexism is manifested in Chinese. The SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type, which can be exploited, among others, for building computational methods to identify and investigate finer-grained gender-related abusive language. We conduct experiments for the three sexism classification tasks making use of state-of-the-art machine learning models. Our results show competitive performance, providing a benchmark for sexism detection in the Chinese language, as well as an error analysis highlighting open challenges need- * This is to indicate the corresponding author.

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018

Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, "You are a good woman" was considered "sexist" when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure gender biases on models trained with different abusive language datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three bias mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce gender bias by 90-98% and can be extended to correct model bias in other scenarios.

arXiv (Cornell University), 2023

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain-Reddit) for multi-level classification into Sexist or not Sexist, and other subsequent subclassifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49 th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052% F1-score. Content warning: All examples of sexism comments used are for illustrative purpose only.

Kalpa publications in computing, 2023

As the world's digital population grows, so does the reach and usage of social media: in 2021, 56% of the global population were social media users [1]. Social networks are now a part of our everyday life and continue to transform the way we interact with others on a global scale The downside is that negative behaviors in social interactions are also increasing their presence. For example, between March 1 and April 30, the OBERAXE (Spanish Observatory of Racism and Xenophobia) has detected a 27% increase in hate speech on social networks with respect to the previous two-month period [2]. In this paper we target the detection and classification of sexist content in social media texts. Two tasks are considered: (i) a binary classification task to decide whether a given text is sexist or not; and (ii) a multiclass classification task according to the type of sexism present in it.

ArXiv, 2021

Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey of 304 papers on gender bias in natural language processing. We analyse definitions of gender and its categories within social sciences and connect them to formal definitions of gender bias in NLP research. We survey lexica and datasets applied in research on gender bias and then compare and contrast approaches to detecting and mitigating gender bias. We find that research on gender bias suffers from four core limitations. 1) Most research treats gender as a binary variable neglecting its fluidity and continuity. 2) Most of the work has been conducted in monolingual setups for English or other high-resource languages. 3) Despite a myriad of papers on gender bias in NLP methods, we find that most of the newly developed algorithms do not test their models for bias and disregard possible ethical considerations...

2021

In this paper, we address the problem of automatic misogyny identification focusing on understanding the representation capabilities of widely adopted embeddings and addressing the problem of unintended bias. The proposed framework, grounded on Sentence Embeddings and Multi-Objective Bayesian Optimization, has been validated on an Italian dataset. We highlight capabilities and weaknesses related to the use of pre-trained language, as well as the contribution of Bayesian Optimization for mitigating the problem of biased predictions.

ceur, 2024

Every day, millions of information are shared on the internet through social media. The contents of the social media posts are based on the person's wishes, emotional expressions, ambitions, passions, and achievements. Among these posts there are possibilities of hurtful messages such as sexist contents, getting embedded. It may sometimes be intentional or unintentional, but also may disturb the mental well-being of the recipient. So automatic identification of these sexist languages and terms in social media posts has to be taken into immediate consideration. EXIST (sEXism Identification in Social Media Network) 2024, a shared task has addressed this issue. This shared task addresses binary classification(Task1), multiclass classification(Task2) and multilabel classification(Task3). We contributed Language Agnostic BERT Sentence Embeddings(LaBSE) based MultiLayer Perceptron (MLP) classifier, eXtreme Gradient Boosting (XGBoost) Classifier, and ensemble Convolutional Neural Network (CNN) model for Task1 and LABSE with MLP classifier and XGBoost Classifier for Task2.

2021

Gender inequality represents a considerable loss of human potential and perpetuates a culture of violence, higher gender wage gaps, and a lack of representation of women in higher and leadership positions. Applications powered by Artificial Intelligence (AI) are increasingly being used in the real world to provide critical decisions about who is going to be hired, granted a loan, admitted to college, etc. However, the main pillars of AI, Natural Language Processing (NLP) and Machine Learning (ML) have been shown to reflect and even amplify gender biases and stereotypes, which are mainly inherited from historical training data. In an effort to facilitate the identification and mitigation of gender bias in English text, we develop a comprehensive taxonomy that relies on the following gender bias types: Generic Pronouns, Sexism, Occupational Bias, Exclusionary Bias, and Semantics. We also provide a bottom-up overview of gender bias, from its societal origin to its spillover onto langua...