Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective (2022.findings-acl)
Copied to clipboard
| Challenge: | Prior research has shown the need to consider community language norms when studying taboo text classification and annotations. |
| Approach: | They propose to use special classifiers tuned for each community's language to study bias in taboo classification and annotation where a community perspective is front and center. |
| Outcome: | The proposed method shows that biases are strongest against African Americans and South Asians . a community perspective is front and center in the proposed method . |
Similar Papers
The Risk of Racial Bias in Hate Speech Detection (P19-1)
Copied to clipboard
| Challenge: | Annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. |
| Approach: | They propose *dialect* and *race priming* as ways to reduce the racial bias in hate speech detection models by detecting differences in dialects in annotated tweets. |
| Outcome: | The proposed models acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. |
Challenges in Automated Debiasing for Toxic Language Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for debiasing toxic language data are limited in their ability to prevent biased behavior in toxic language detection systems. |
| Approach: | They propose to debiase toxic language detection models using lexical and dialectal markers using synthetic labels instead of traditional methods. |
| Outcome: | The proposed method reduces dialectal associations with toxicity despite the use of synthetic labels . |
Detecting Community Sensitive Norm Violations in Online Conversations (2021.findings-emnlp)
Copied to clipboard
Chan Young Park, Julia Mendelsohn, Karthik Radhakrishnan, Kinjal Jain, Tushar Kanakagiri, David Jurgens, Yulia Tsvetkov
| Challenge: | Existing efforts to identify unacceptable behavior have focused on toxicity as the sole form of community norm violation. |
| Approach: | They propose a dataset that focuses on a more complete spectrum of community norms and their violations in local conversational and global contexts. |
| Outcome: | The proposed model improves the detection of community norm violations in local conversational and global contexts. |
Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets (2020.lrec-1)
Copied to clipboard
| Challenge: | a recent study shows that many definitions are being used for equivalent concepts, making most datasets incompatible. |
| Approach: | They analyze six publicly available datasets to determine their similarity and compatibility . they propose to use Fast Text word vectors to analyze similarity between different datasets . |
| Outcome: | The proposed model performs better on similar datasets and worse on more non-offensive samples. |
Social Bias Probing: Fairness Benchmarking for Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for evaluating social biases in language models have been limited to binary association tests on small datasets. |
| Approach: | They propose a framework for probing language models for social biases by assessing disparate treatment . they use a large-scale benchmark to examine the diversity of identities and stereotypes . |
| Outcome: | The proposed framework expands the analysis beyond the binary comparison of stereotypical versus anti-stereotypical identities to include a diverse range of identities and stereotypes. |
The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (2026.acl-long)
Copied to clipboard
Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, null Mahongxia, Yanghua Xiao
| Challenge: | Existing cultural benchmarks assess cultural knowledge or values biases, but ignore cultural taboos. |
| Approach: | They propose a benchmark to evaluate and improve the cultural taboo safety of large language models. |
| Outcome: | The proposed benchmark spans 77 countries and regions, and includes over 2,020 taboos. |
Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022.findings-emnlp)
Copied to clipboard
Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng
| Challenge: | a number of safety concerns hinder the deployment of open-domain dialog systems, such as offensive languages and toxic behaviors, such social bias is difficult to detect. |
| Approach: | They propose a Dial-Bias Framework for analyzing social bias in conversations . they introduce a Chinese social bias dialog dataset and conduct in-depth ablation studies . |
| Outcome: | The proposed framework is the first annotated Chinese social bias dialog dataset . the proposed framework also provides a fine-grained dialog bias measurement benchmark . |
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)
Copied to clipboard
Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha
| Challenge: | 59,152 comments are annotated with a hierarchical, fine-grained taget marking aggression and bias of various kinds on social media platforms. |
| Approach: | They propose to annotate a multilingual dataset with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. |
| Outcome: | The proposed dataset contains 59,152 comments in four languages, mostly code-mixed with English. |
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (2020.emnlp-main)
Copied to clipboard
| Challenge: | a recent study has shown that data collection is neglected by ignoring the quality of data. |
| Approach: | They propose to use latent semantics to evaluate selection bias in hate speech . they compare latent Dirichlet Allocation (LDA) to eleven hate speech corpora . |
| Outcome: | The proposed method could be revisable before focusing on classification performance. |
Global Voices, Local Biases: Socio-Cultural Prejudices across Languages (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on human biases are heavily skewed towards Western and European languages . despite growing interest in language models, there are several shortcomings in the literature . |
| Approach: | They scale the Word Embedding Association Test to 24 languages and add culturally relevant information for each language. |
| Outcome: | The proposed language models can reflect and often amplify the effects of bias across linguistic, cultural, and societal borders. |