449 research outputs found
An analysis of unconscious gender bias in academic texts by means of a decision algorithm
Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms
ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
The convenience of social media has also enabled its misuse, potentially
resulting in toxic behavior. Nearly 66% of internet users have observed online
harassment, and 41% claim personal experience, with 18% facing severe forms of
online harassment. This toxic communication has a significant impact on the
well-being of young individuals, affecting mental health and, in some cases,
resulting in suicide. These communications exhibit complex linguistic and
contextual characteristics, making recognition of such narratives challenging.
In this paper, we provide a multimodal dataset of toxic social media
interactions between confirmed high school students, called ALONE (AdoLescents
ON twittEr), along with descriptive explanation. Each instance of interaction
includes tweets, images, emoji and related metadata. Our observations show that
individual tweets do not provide sufficient evidence for toxic behavior, and
meaningful use of context in interactions can enable highlighting or
exonerating tweets with purported toxicity.Comment: Accepted: Social Informatics 202
ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive explanation. Each instance of interaction includes tweets, images, emoji and related metadata. Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models
Objective. Chemical named entity recognition (NER) models have the potential
to impact a wide range of downstream tasks, from identifying adverse drug
reactions to general pharmacoepidemiology. However, it is unknown whether these
models work the same for everyone. Performance disparities can potentially
cause harm rather than the intended good. Hence, in this paper, we measure
gender-related performance disparities of chemical NER systems.
Materials and Methods. We develop a framework to measure gender bias in
chemical NER models using synthetic data and a newly annotated dataset of over
92,405 words with self-identified gender information from Reddit. We applied
and evaluated state-of-the-art biomedical NER models.
Results. Our findings indicate that chemical NER models are biased. The
results of the bias tests on the synthetic dataset and the real-world data
multiple fairness issues. For example, for synthetic data, we find that
female-related names are generally classified as chemicals, particularly in
datasets containing many brand names rather than standard ones. For both
datasets, we find consistent fairness issues resulting in substantial
performance disparities between female- and male-related data.
Discussion. Our study highlights the issue of biases in chemical NER models.
For example, we find that many systems cannot detect contraceptives (e.g.,
birth control).
Conclusion. Chemical NER models are biased and can be harmful to
female-related groups. Therefore, practitioners should carefully consider the
potential biases of these models and take steps to mitigate them
Hate Speech Classifiers Learn Normative Social Stereotypes
AbstractSocial stereotypes negatively impact individualsâ judgments about different groups and may have a critical role in understanding language directed toward marginalized groups. Here, we assess the role of social stereotypes in the automated detection of hate speech in the English language by examining the impact of social stereotypes on annotation behaviors, annotated datasets, and hate speech classifiers. Specifically, we first investigate the impact of novice annotatorsâ stereotypes on their hate-speech-annotation behavior. Then, we examine the effect of normative stereotypes in language on the aggregated annotatorsâ judgments in a large annotated corpus. Finally, we demonstrate how normative stereotypes embedded in language resources are associated with systematic prediction errors in a hate-speech classifier. The results demonstrate that hate-speech classifiers reflect social stereotypes against marginalized groups, which can perpetuate social inequalities when propagated at scale. This framework, combining social-psychological and computational-linguistic methods, provides insights into sources of bias in hate-speech moderation, informing ongoing debates regarding machine learning fairness
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model
Disparate biases associated with datasets and trained classifiers in hateful
and abusive content identification tasks have raised many concerns recently.
Although the problem of biased datasets on abusive language detection has been
addressed more frequently, biases arising from trained classifiers have not yet
been a matter of concern. Here, we first introduce a transfer learning approach
for hate speech detection based on an existing pre-trained language model
called BERT and evaluate the proposed model on two publicly available datasets
annotated for racism, sexism, hate or offensive content on Twitter. Next, we
introduce a bias alleviation mechanism in hate speech detection task to
mitigate the effect of bias in training set during the fine-tuning of our
pre-trained BERT-based model. Toward that end, we use an existing
regularization method to reweight input samples, thereby decreasing the effects
of high correlated training set' s n-grams with class labels, and then
fine-tune our pre-trained BERT-based model with the new re-weighted samples. To
evaluate our bias alleviation mechanism, we employ a cross-domain approach in
which we use the trained classifiers on the aforementioned datasets to predict
the labels of two new datasets from Twitter, AAE-aligned and White-aligned
groups, which indicate tweets written in African-American English (AAE) and
Standard American English (SAE) respectively. The results show the existence of
systematic racial bias in trained classifiers as they tend to assign tweets
written in AAE from AAE-aligned group to negative classes such as racism,
sexism, hate, and offensive more often than tweets written in SAE from
White-aligned. However, the racial bias in our classifiers reduces
significantly after our bias alleviation mechanism is incorporated. This work
could institute the first step towards debiasing hate speech and abusive
language detection systems.Comment: This paper has been accepted in the PLOS ONE journal in August 202
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