Automated Identification of Sexual Orientation and Gender Identity Discriminatory Texts from Issue Comments
- URL: http://arxiv.org/abs/2311.08485v2
- Date: Mon, 28 Jul 2025 16:53:22 GMT
- Title: Automated Identification of Sexual Orientation and Gender Identity Discriminatory Texts from Issue Comments
- Abstract summary: This study aims to develop an automated mechanism to identify Sexual orientation and Gender identity Discriminatory (SGID) texts from software developers' communications.<n>SGID4SE incorporates six preprocessing steps and ten state-of-the-art algorithms.
- Score: 3.134071086568746
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In an industry dominated by straight men, many developers representing other gender identities and sexual orientations often encounter hateful or discriminatory messages. Such communications pose barriers to participation for women and LGBTQ+ persons. Due to sheer volume, manual inspection of all communications for discriminatory communication is infeasible for a large-scale Free Open-Source Software (FLOSS) community. To address this challenge, this study aims to develop an automated mechanism to identify Sexual orientation and Gender identity Discriminatory (SGID) texts from software developers' communications. On this goal, we trained and evaluated SGID4SE ( Sexual orientation and Gender Identity Discriminatory text identification for (4) Software Engineering texts) as a supervised learning-based SGID detection tool. SGID4SE incorporates six preprocessing steps and ten state-of-the-art algorithms. SGID4SE implements six different strategies to improve the performance of the minority class. We empirically evaluated each strategy and identified an optimum configuration for each algorithm. In our ten-fold cross-validation-based evaluations, a BERT-based model boosts the best performance with 85.9% precision, 80.0% recall, and 82.9% F1-Score for the SGID class. This model achieves 95.7% accuracy and 80.4% Matthews Correlation Coefficient. Our dataset and tool establish a foundation for further research in this direction.
Related papers
- Efficient Test-Time Adaptation through Human-AI Interaction [112.5131603022122]
We propose test-time adaptation through human-agent interaction (TAHI)<n>We adapt agents to 30 individuals in two high-utility domains, writing and visual creation, on a total of 600 tasks.<n>Our agents improve solo task success by 4.5-20.9% within only tens of tasks.
arXiv Detail & Related papers (2026-09-03T17:33:18Z) - Investigating Gender Bias in Touch Biometrics [2.2228471493209994]
This paper investigates gender bias in swipe-based authentication using the BBMAS (117 users) and ANTAL (71 users) datasets.<n>XGBoost achieved authentication accuracies of 92% and 94% on the BBMAS and ANTAL datasets, respectively.<n>No significant gender differences in authentication error rates across almost all experimental settings.
arXiv Detail & Related papers (2026-06-09T21:19:42Z) - NearID: Identity Representation Learning via Near-identity Distractors [116.71529576796605]
Existing vision encoders entangle object identity with background context, leading to unreliable representations and metrics.<n>We introduce the first principled framework to address this vulnerability using Near-identity distractors.<n>We present the NearID dataset (19K identities, 316K matched-context distractors) together with a strict margin-based evaluation protocol.
arXiv Detail & Related papers (2026-04-02T12:33:14Z) - Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait [70.00430652562012]
FarSight is an end-to-end system for person recognition that integrates biometric cues across face, gait, and body shape modalities.<n>FarSight incorporates novel algorithms across four core modules: multi-subject detection and tracking, recognition-aware video restoration, modality-specific biometric feature encoding, and quality-guided multi-modal fusion.
arXiv Detail & Related papers (2025-05-07T17:58:25Z) - GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases.
GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
arXiv Detail & Related papers (2024-08-22T15:35:46Z) - IdentiFace : A VGG Based Multimodal Facial Biometric System [0.0]
"IdentiFace" is a multimodal facial biometric system that combines the core of facial recognition with some of the most important soft biometric traits such as gender, face shape, and emotion.
For the recognition problem, we acquired a 99.2% test accuracy for five classes with high intra-class variations using data collected from the FERET database.
We were also able to achieve a testing accuracy of 88.03% in the face-shape problem using the celebrity face-shape dataset.
arXiv Detail & Related papers (2024-01-02T14:36:28Z) - Whole-body Detection, Recognition and Identification at Altitude and
Range [57.445372305202405]
We propose an end-to-end system evaluated on diverse datasets.
Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images.
We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios.
arXiv Detail & Related papers (2023-11-09T20:20:23Z) - GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning [4.831663144935878]
We propose DenseGait, the largest dataset for pretraining gait analysis systems containing 217K anonymized tracklets.
We also propose GaitFormer, a transformer-based model that achieves 92.5% accuracy on CASIA-B and 85.33% on FVG.
arXiv Detail & Related papers (2023-10-30T10:28:44Z) - One-Shot Learning for Periocular Recognition: Exploring the Effect of
Domain Adaptation and Data Bias on Deep Representations [59.17685450892182]
We investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition.
We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images.
Traditional algorithms like SIFT can outperform CNNs in situations with limited data.
arXiv Detail & Related papers (2023-07-11T09:10:16Z) - Improving CNN-based Person Re-identification using score Normalization [2.462953128215087]
This paper proposes a novel approach for PRe-ID, which combines a CNN based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning.
The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, VIPeR and PRID450S.
arXiv Detail & Related papers (2023-07-01T18:12:27Z) - Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - Deep learning for identification and face, gender, expression
recognition under constraints [1.2647816797166165]
Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images.
The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile.
arXiv Detail & Related papers (2021-11-02T22:45:09Z) - Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by
Ranking Algorithms [68.85295025020942]
We propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a Search Engines to support gender stereotypes.
GSR is the first specifically tailored measure for Information Retrieval, capable of quantifying representational harms.
arXiv Detail & Related papers (2020-09-02T20:45:04Z) - Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases.
Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
arXiv Detail & Related papers (2020-04-15T15:58:05Z)
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.