{"total":59,"rows":[{"id":"2106.03521","title":"RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models","created":"2021-06-07","cited_by_count":149,"area":null},{"id":"2112.14168","title":"A Survey on Gender Bias in Natural Language Processing","created":"2021-12-28","cited_by_count":89,"area":null},{"id":"2205.02526","title":"Theories of \"Gender\" in NLP Bias Research","created":"2022-05-05","cited_by_count":62,"area":null},{"id":"2104.07496","title":"Unmasking the Mask -- Evaluating Social Biases in Masked Language Models","created":"2021-04-15","cited_by_count":60,"area":null},{"id":"2205.05092","title":"Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words","created":"2022-05-10","cited_by_count":49,"area":null},{"id":"2207.04153","title":"Probing Classifiers are Unreliable for Concept Removal and Detection","created":"2022-07-08","cited_by_count":41,"area":null},{"id":"2110.00672","title":"Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models","created":"2021-10-01","cited_by_count":39,"area":null},{"id":"2203.07893","title":"Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information","created":"2022-03-15","cited_by_count":34,"area":null},{"id":"2210.14975","title":"MABEL: Attenuating Gender Bias using Textual Entailment Data","created":"2022-10-26","cited_by_count":31,"area":null},{"id":"2210.11471","title":"Choose Your Lenses: Flaws in Gender Bias Evaluation","created":"2022-10-20","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":"2101.09525","title":"Dictionary-based Debiasing of Pre-trained Word Embeddings","created":"2021-01-23","cited_by_count":24,"area":null},{"id":"2104.08465","title":"Frequency-based Distortions in Contextualized Word Embeddings","created":"2021-04-17","cited_by_count":20,"area":null},{"id":"2406.19097","title":"Fairness and Bias in Multimodal AI: A Survey","created":"2024-06-27","cited_by_count":20,"area":"mllm"},{"id":"2106.08680","title":"Evaluating Gender Bias in Hindi-English Machine Translation","created":"2021-06-16","cited_by_count":19,"area":null},{"id":"2109.03158","title":"Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles","created":"2021-09-07","cited_by_count":18,"area":null},{"id":"2203.07523","title":"Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings","created":"2022-03-14","cited_by_count":18,"area":null},{"id":"2212.04273","title":"Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection","created":"2022-12-08","cited_by_count":17,"area":null},{"id":"2207.10245","title":"The Birth of Bias: A case study on the evolution of gender bias in an English language model","created":"2022-07-21","cited_by_count":16,"area":null},{"id":"2302.12578","title":"Fairness in Language Models Beyond English: Gaps and Challenges","created":"2023-02-24","cited_by_count":16,"area":null},{"id":"2109.04732","title":"Assessing the Reliability of Word Embedding Gender Bias Measures","created":"2021-09-10","cited_by_count":14,"area":null},{"id":"2108.06295","title":"Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biases","created":"2021-08-13","cited_by_count":13,"area":null},{"id":"2204.06827","title":"How Gender Debiasing Affects Internal Model Representations, and Why It Matters","created":"2022-04-14","cited_by_count":12,"area":null},{"id":"2307.04303","title":"Learning to Generate Equitable Text in Dialogue from Biased Training Data","created":"2023-07-10","cited_by_count":12,"area":null},{"id":"2110.07654","title":"Residual2Vec: Debiasing graph embedding with random graphs","created":"2021-10-14","cited_by_count":11,"area":null},{"id":"2301.12074","title":"Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples","created":"2023-01-28","cited_by_count":8,"area":null},{"id":"2111.07864","title":"Evaluating Metrics for Bias in Word Embeddings","created":"2021-11-15","cited_by_count":7,"area":null},{"id":"2205.05093","title":"Richer Countries and Richer Representations","created":"2022-05-10","cited_by_count":7,"area":null},{"id":"2212.11672","title":"Trustworthy Social Bias Measurement","created":"2022-12-20","cited_by_count":7,"area":null},{"id":"2205.05901","title":"Mitigating Gender Stereotypes in Hindi and Marathi","created":"2022-05-12","cited_by_count":6,"area":null},{"id":"2211.08461","title":"Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models","created":"2022-11-15","cited_by_count":6,"area":null},{"id":"2310.11991","title":"Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation","created":"2023-10-18","cited_by_count":6,"area":null},{"id":"2107.10413","title":"Theoretical foundations and limits of word embeddings: what types of meaning can they capture?","created":"2021-07-22","cited_by_count":5,"area":null},{"id":"2206.01691","title":"Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals","created":"2022-06-03","cited_by_count":5,"area":null},{"id":"2211.03634","title":"No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media","created":"2022-11-07","cited_by_count":5,"area":null},{"id":"2211.04256","title":"Bridging Fairness and Environmental Sustainability in Natural Language Processing","created":"2022-11-08","cited_by_count":5,"area":null},{"id":"2305.10610","title":"Solving Cosine Similarity Underestimation between High Frequency Words by L2 Norm Discounting","created":"2023-05-17","cited_by_count":5,"area":null},{"id":"2406.12165","title":"Statistical Uncertainty in Word Embeddings: GloVe-V","created":"2024-06-18","cited_by_count":5,"area":null},{"id":"2104.06474","title":"On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach","created":"2021-04-13","cited_by_count":4,"area":null},{"id":"2208.02052","title":"Large scale analysis of gender bias and sexism in song lyrics","created":"2022-08-03","cited_by_count":4,"area":null},{"id":"2211.13709","title":"Undesirable Biases in NLP: Addressing Challenges of Measurement","created":"2022-11-24","cited_by_count":4,"area":null},{"id":"2310.12936","title":"A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models","created":"2023-10-19","cited_by_count":4,"area":null},{"id":"2406.13556","title":"Evaluating Short-Term Temporal Fluctuations of Social Biases in Social Media Data and Masked Language Models","created":"2024-06-19","cited_by_count":3,"area":null},{"id":"2408.01966","title":"ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science","created":"2024-08-04","cited_by_count":3,"area":null},{"id":"2305.19294","title":"Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity","created":"2023-05-30","cited_by_count":2,"area":null},{"id":"2402.13954","title":"Measuring Social Biases in Masked Language Models by Proxy of Prediction Quality","created":"2024-02-21","cited_by_count":2,"area":null},{"id":"2508.09450","title":"From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text","created":"2025-08-13","cited_by_count":2,"area":null},{"id":"2509.22699","title":"Are you sure? Measuring models bias in content moderation through uncertainty","created":"2025-09-21","cited_by_count":2,"area":null},{"id":"2201.08451","title":"Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency","created":"2022-01-20","cited_by_count":1,"area":null},{"id":"2305.09548","title":"Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing","created":"2023-05-16","cited_by_count":1,"area":null},{"id":"2310.16897","title":"Divide et Impera: Multi-Transformer Architectures for Complex NLP-Tasks","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2407.06917","title":"Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models","created":"2024-07-09","cited_by_count":1,"area":null},{"id":"2501.04316","title":"Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts","created":"2025-01-08","cited_by_count":1,"area":null},{"id":"2203.14603","title":"The SAME score: Improved cosine based bias score for word embeddings","created":"2022-03-28","cited_by_count":0,"area":null},{"id":"2206.01767","title":"[Re] Badder Seeds: Reproducing the Evaluation of Lexical Methods for Bias Measurement","created":"2022-06-03","cited_by_count":0,"area":null},{"id":"2305.14574","title":"Detecting and Mitigating Indirect Stereotypes in Word Embeddings","created":"2023-05-23","cited_by_count":0,"area":null},{"id":"2401.15499","title":"Semantic Properties of cosine based bias scores for word embeddings","created":"2024-01-27","cited_by_count":0,"area":null},{"id":"2404.02205","title":"A Holistic Indicator of Polarization to Measure Online Sexism","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2609.29730","title":"The Gold in Bias: Maturing the AI Design Process through Verification","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}