{"total":195,"rows":[{"id":"2110.08193","title":"BBQ: A Hand-Built Bias Benchmark for Question Answering","created":"2021-10-15","cited_by_count":701,"area":null},{"id":"2105.03023","title":"DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts","created":"2021-05-07","cited_by_count":441,"area":null},{"id":"2208.03274","title":"A Holistic Approach to Undesired Content Detection in the Real World","created":"2022-08-05","cited_by_count":356,"area":null},{"id":"2110.05719","title":"Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations","created":"2021-10-12","cited_by_count":314,"area":null},{"id":"2111.07997","title":"Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection","created":"2021-11-15","cited_by_count":264,"area":null},{"id":"2109.05322","title":"Latent Hatred: A Benchmark for Understanding Implicit Hate Speech","created":"2021-09-11","cited_by_count":217,"area":null},{"id":"2310.19736","title":"Evaluating Large Language Models: A Comprehensive Survey","created":"2023-10-30","cited_by_count":202,"area":null},{"id":"2202.11176","title":"A New Generation of Perspective API: Efficient Multilingual Character-level Transformers","created":"2022-02-22","cited_by_count":185,"area":null},{"id":"2109.07445","title":"Challenges in Detoxifying Language Models","created":"2021-09-15","cited_by_count":169,"area":null},{"id":"2304.03738","title":"Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models","created":"2023-04-07","cited_by_count":148,"area":null},{"id":"2102.12060","title":"Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing","created":"2021-02-24","cited_by_count":126,"area":null},{"id":"2106.08503","title":"Understanding and Evaluating Racial Biases in Image Captioning","created":"2021-06-16","cited_by_count":108,"area":null},{"id":"2101.09995","title":"Re-imagining Algorithmic Fairness in India and Beyond","created":"2021-01-25","cited_by_count":104,"area":null},{"id":"2102.00086","title":"Challenges in Automated Debiasing for Toxic Language Detection","created":"2021-01-29","cited_by_count":96,"area":null},{"id":"2306.01943","title":"NLPositionality: Characterizing Design Biases of Datasets and Models","created":"2023-06-02","cited_by_count":94,"area":null},{"id":"2306.05949","title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","created":"2023-06-09","cited_by_count":92,"area":null},{"id":"2104.08646","title":"Competency Problems: On Finding and Removing Artifacts in Language Data","created":"2021-04-17","cited_by_count":91,"area":null},{"id":"2109.05184","title":"MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets","created":"2021-09-11","cited_by_count":86,"area":null},{"id":"2106.14574","title":"Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics","created":"2021-06-28","cited_by_count":83,"area":null},{"id":"2108.03362","title":"On Measures of Biases and Harms in NLP","created":"2021-08-07","cited_by_count":81,"area":null},{"id":"2106.11410","title":"A Survey of Race, Racism, and Anti-Racism in NLP","created":"2021-06-21","cited_by_count":75,"area":null},{"id":"2110.08466","title":"On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark","created":"2021-10-16","cited_by_count":73,"area":null},{"id":"2205.00501","title":"Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation","created":"2022-05-01","cited_by_count":69,"area":null},{"id":"2102.08886","title":"Towards generalisable hate speech detection: a review on obstacles and solutions","created":"2021-02-17","cited_by_count":66,"area":null},{"id":"2202.00126","title":"Handling Bias in Toxic Speech Detection: A Survey","created":"2022-01-26","cited_by_count":61,"area":null},{"id":"2103.12407","title":"Detecting Hate Speech with GPT-3","created":"2021-03-23","cited_by_count":56,"area":null},{"id":"2209.12226","title":"Re-contextualizing Fairness in NLP: The Case of India","created":"2022-09-25","cited_by_count":55,"area":null},{"id":"2212.08011","title":"Multi-VALUE: A Framework for Cross-Dialectal English NLP","created":"2022-12-15","cited_by_count":53,"area":null},{"id":"2105.13947","title":"Changing the World by Changing the Data","created":"2021-05-28","cited_by_count":51,"area":null},{"id":"2206.09917","title":"Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models","created":"2022-06-20","cited_by_count":51,"area":null},{"id":"2304.12397","title":"On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research","created":"2023-04-24","cited_by_count":44,"area":null},{"id":"2211.03433","title":"Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering","created":"2022-11-07","cited_by_count":40,"area":null},{"id":"2108.05927","title":"Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages","created":"2021-08-12","cited_by_count":38,"area":null},{"id":"2101.11974","title":"Disembodied Machine Learning: On the Illusion of Objectivity in NLP","created":"2021-01-28","cited_by_count":37,"area":null},{"id":"2202.08011","title":"Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks","created":"2022-02-16","cited_by_count":36,"area":null},{"id":"2305.11242","title":"Comparing Biases and the Impact of Multilingual Training across Multiple Languages","created":"2023-05-18","cited_by_count":36,"area":null},{"id":"2305.14291","title":"Evaluation of African American Language Bias in Natural Language Generation","created":"2023-05-23","cited_by_count":36,"area":null},{"id":"2404.14397","title":"RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?","created":"2024-04-22","cited_by_count":36,"area":null},{"id":"2110.14375","title":"Perceptual Score: What Data Modalities Does Your Model Perceive?","created":"2021-10-27","cited_by_count":35,"area":null},{"id":"2306.01985","title":"COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements","created":"2023-06-03","cited_by_count":34,"area":null},{"id":"2206.03216","title":"Data Governance in the Age of Large-Scale Data-Driven Language Technology","created":"2022-05-04","cited_by_count":33,"area":null},{"id":"2104.06999","title":"Detecting Cross-Geographic Biases in Toxicity Modeling on Social Media","created":"2021-04-14","cited_by_count":31,"area":null},{"id":"2311.00321","title":"HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning","created":"2023-11-01","cited_by_count":31,"area":null},{"id":"2406.12223","title":"ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations","created":"2024-06-18","cited_by_count":31,"area":null},{"id":"2109.04953","title":"Does Pretraining for Summarization Require Knowledge Transfer?","created":"2021-09-10","cited_by_count":30,"area":null},{"id":"2204.01440","title":"Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study","created":"2022-04-04","cited_by_count":30,"area":null},{"id":"2204.01734","title":"On Explaining Multimodal Hateful Meme Detection Models","created":"2022-04-04","cited_by_count":29,"area":null},{"id":"2306.08158","title":"Sociodemographic Bias in Language Models: A Survey and Forward Path","created":"2023-06-13","cited_by_count":28,"area":null},{"id":"2106.05664","title":"Ruddit: Norms of Offensiveness for English Reddit Comments","created":"2021-06-10","cited_by_count":27,"area":null},{"id":"2407.08188","title":"Position: Measure Dataset Diversity, Don't Just Claim It","created":"2024-07-11","cited_by_count":26,"area":null},{"id":"2105.02590","title":"Reliability Testing for Natural Language Processing Systems","created":"2021-05-06","cited_by_count":25,"area":null},{"id":"2404.17874","title":"From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets","created":"2024-04-27","cited_by_count":25,"area":null},{"id":"2203.11764","title":"Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments","created":"2022-03-22","cited_by_count":22,"area":null},{"id":"2112.07868","title":"Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases","created":"2021-12-15","cited_by_count":21,"area":null},{"id":"2311.08391","title":"A Material Lens on Coloniality in NLP","created":"2023-11-14","cited_by_count":21,"area":null},{"id":"2109.00227","title":"Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments","created":"2021-09-01","cited_by_count":20,"area":null},{"id":"2109.13137","title":"Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework","created":"2021-09-27","cited_by_count":19,"area":null},{"id":"2206.15455","title":"Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions","created":"2022-06-30","cited_by_count":18,"area":null},{"id":"2210.15144","title":"Gendered Mental Health Stigma in Masked Language Models","created":"2022-10-27","cited_by_count":18,"area":null},{"id":"2309.08573","title":"Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models","created":"2023-09-15","cited_by_count":18,"area":null},{"id":"2206.04007","title":"Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization","created":"2022-06-08","cited_by_count":17,"area":null},{"id":"2109.10052","title":"Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?","created":"2021-09-21","cited_by_count":16,"area":null},{"id":"2406.00020","title":"Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Bias","created":"2024-05-23","cited_by_count":16,"area":null},{"id":"2205.11315","title":"KOLD: Korean Offensive Language Dataset","created":"2022-05-23","cited_by_count":14,"area":null},{"id":"2205.01038","title":"Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness","created":"2022-04-18","cited_by_count":13,"area":null},{"id":"2206.13757","title":"Flexible text generation for counterfactual fairness probing","created":"2022-06-28","cited_by_count":13,"area":null},{"id":"2305.13406","title":"DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules","created":"2023-05-22","cited_by_count":13,"area":null},{"id":"2311.14126","title":"Towards Auditing Large Language Models: Improving Text-based Stereotype Detection","created":"2023-11-23","cited_by_count":13,"area":null},{"id":"2503.01623","title":"Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations","created":"2025-03-03","cited_by_count":13,"area":null},{"id":"2105.08847","title":"Beyond \"Fairness:\" Structural (In)justice Lenses on AI for Education","created":"2021-05-18","cited_by_count":12,"area":null},{"id":"2110.14839","title":"Hate Speech Classifiers Learn Human-Like Social Stereotypes","created":"2021-10-28","cited_by_count":12,"area":null},{"id":"2205.04525","title":"Towards a multi-stakeholder value-based assessment framework for algorithmic systems","created":"2022-05-09","cited_by_count":12,"area":null},{"id":"2210.11762","title":"Detecting Unintended Social Bias in Toxic Language Datasets","created":"2022-10-21","cited_by_count":12,"area":null},{"id":"2212.10405","title":"AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection","created":"2022-12-20","cited_by_count":12,"area":null},{"id":"2305.17013","title":"D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias","created":"2023-05-26","cited_by_count":12,"area":null},{"id":"2310.12318","title":"The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis","created":"2023-10-18","cited_by_count":12,"area":null},{"id":"2202.04718","title":"Designing Closed Human-in-the-loop Deferral Pipelines","created":"2022-02-09","cited_by_count":11,"area":null},{"id":"2412.06134","title":"Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings","created":"2024-12-09","cited_by_count":10,"area":null},{"id":"2106.09775","title":"An Information Retrieval Approach to Building Datasets for Hate Speech Detection","created":"2021-06-17","cited_by_count":9,"area":null},{"id":"2305.11348","title":"In the Name of Fairness: Assessing the Bias in Clinical Record De-identification","created":"2023-05-18","cited_by_count":9,"area":null},{"id":"2311.00915","title":"Task-Agnostic Low-Rank Adapters for Unseen English Dialects","created":"2023-11-02","cited_by_count":9,"area":null},{"id":"2102.03671","title":"From Toxicity in Online Comments to Incivility in American News: Proceed with Caution","created":"2021-02-06","cited_by_count":8,"area":null},{"id":"2106.05903","title":"Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate","created":"2021-06-10","cited_by_count":8,"area":null},{"id":"2206.11484","title":"Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models","created":"2022-06-23","cited_by_count":8,"area":null},{"id":"2207.10032","title":"Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals","created":"2022-06-15","cited_by_count":8,"area":null},{"id":"2408.08411","title":"Rater Cohesion and Quality from a Vicarious Perspective","created":"2024-08-15","cited_by_count":8,"area":null},{"id":"2108.04616","title":"Hope Speech detection in under-resourced Kannada language","created":"2021-08-10","cited_by_count":7,"area":null},{"id":"2205.06621","title":"Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes","created":"2022-05-13","cited_by_count":7,"area":null},{"id":"2211.00243","title":"Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection","created":"2022-11-01","cited_by_count":7,"area":null},{"id":"2306.00374","title":"CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation","created":"2023-06-01","cited_by_count":7,"area":null},{"id":"2307.12418","title":"HateModerate: Testing Hate Speech Detectors against Content Moderation Policies","created":"2023-07-23","cited_by_count":7,"area":null},{"id":"2506.08477","title":"Read as You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme Detection","created":"2025-06-10","cited_by_count":7,"area":null},{"id":"2101.05494","title":"Hostility Detection in Hindi leveraging Pre-Trained Language Models","created":"2021-01-14","cited_by_count":6,"area":null},{"id":"2107.12866","title":"Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach","created":"2021-07-27","cited_by_count":6,"area":null},{"id":"2108.02662","title":"Reducing Unintended Bias of ML Models on Tabular and Textual Data","created":"2021-08-05","cited_by_count":6,"area":null},{"id":"2205.01374","title":"Hidden behind the obvious: misleading keywords and implicitly abusive language on social media","created":"2022-05-03","cited_by_count":6,"area":null},{"id":"2302.02038","title":"Rating Sentiment Analysis Systems for Bias through a Causal Lens","created":"2023-02-04","cited_by_count":6,"area":null},{"id":"2305.19409","title":"Examining risks of racial biases in NLP tools for child protective services","created":"2023-05-30","cited_by_count":6,"area":null},{"id":"2505.12349","title":"Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds","created":"2025-05-18","cited_by_count":6,"area":null},{"id":"2101.11155","title":"Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media","created":"2021-01-27","cited_by_count":5,"area":null}],"next_cursor":"100"}