{"list":"other","total":119,"rows":[{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1607.06520","title":"Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings","created":"2016-07-21","cited_by_count":1312},{"id":"2005.14050","title":"Language (Technology) is Power: A Critical Survey of \"Bias\" in NLP","created":"2020-05-28","cited_by_count":956},{"id":"2004.09456","title":"StereoSet: Measuring stereotypical bias in pretrained language models","created":"2020-04-20","cited_by_count":924},{"id":"1608.07187","title":"Semantics derived automatically from language corpora contain human-like biases","created":"2016-08-25","cited_by_count":901},{"id":"1801.07593","title":"Mitigating Unwanted Biases with Adversarial Learning","created":"2018-01-22","cited_by_count":663},{"id":"1804.06876","title":"Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods","created":"2018-04-18","cited_by_count":659},{"id":"1909.01326","title":"The Woman Worked as a Babysitter: On Biases in Language Generation","created":"2019-09-03","cited_by_count":541},{"id":"2004.07667","title":"Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection","created":"2020-04-16","cited_by_count":455},{"id":"1804.09301","title":"Gender Bias in Coreference Resolution","created":"2018-04-25","cited_by_count":442},{"id":"1707.09457","title":"Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints","created":"2017-07-29","cited_by_count":420},{"id":"1903.10561","title":"On Measuring Social Biases in Sentence Encoders","created":"2019-03-25","cited_by_count":398},{"id":"1911.03891","title":"Social Bias Frames: Reasoning about Social and Power Implications of Language","created":"2019-11-10","cited_by_count":380},{"id":"1901.09451","title":"Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting","created":"2019-01-27","cited_by_count":349},{"id":"1906.08976","title":"Mitigating Gender Bias in Natural Language Processing: Literature Review","created":"2019-06-21","cited_by_count":287},{"id":"1906.07337","title":"Measuring Bias in Contextualized Word Representations","created":"2019-06-18","cited_by_count":276},{"id":"1903.03862","title":"Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them","created":"2019-03-09","cited_by_count":268},{"id":"1711.08412","title":"Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes","created":"2017-11-22","cited_by_count":253},{"id":"1906.00591","title":"Evaluating Gender Bias in Machine Translation","created":"2019-06-03","cited_by_count":249},{"id":"1803.09797","title":"Women also Snowboard: Overcoming Bias in Captioning Models","created":"2018-03-26","cited_by_count":236},{"id":"1805.04508","title":"Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems","created":"2018-05-11","cited_by_count":234},{"id":"1904.03035","title":"Identifying and Reducing Gender Bias in Word-Level Language Models","created":"2019-04-05","cited_by_count":214},{"id":"1807.11714","title":"Gender Bias in Neural Natural Language Processing","created":"2018-07-31","cited_by_count":211},{"id":"1904.03310","title":"Gender Bias in Contextualized Word Embeddings","created":"2019-04-05","cited_by_count":203},{"id":"1906.04571","title":"Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology","created":"2019-06-11","cited_by_count":200},{"id":"2004.12265","title":"Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias","created":"2020-04-26","cited_by_count":191},{"id":"2007.08100","title":"Towards Debiasing Sentence Representations","created":"2020-07-16","cited_by_count":171},{"id":"1809.01496","title":"Learning Gender-Neutral Word Embeddings","created":"2018-08-29","cited_by_count":169},{"id":"1808.07231","title":"Reducing Gender Bias in Abusive Language Detection","created":"2018-08-22","cited_by_count":152},{"id":"1911.03842","title":"Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation","created":"2019-11-10","cited_by_count":152},{"id":"1911.03064","title":"Reducing Sentiment Bias in Language Models via Counterfactual Evaluation","created":"2019-11-08","cited_by_count":148},{"id":"2012.15859","title":"Intrinsic Bias Metrics Do Not Correlate with Application Bias","created":"2020-12-31","cited_by_count":148},{"id":"1904.04047","title":"Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings","created":"2019-04-03","cited_by_count":147},{"id":"1911.01485","title":"Assessing Social and Intersectional Biases in Contextualized Word Representations","created":"2019-11-04","cited_by_count":135},{"id":"2108.12084","title":"Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies","created":"2021-08-27","cited_by_count":128},{"id":"1809.02208","title":"Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate","created":"2018-09-06","cited_by_count":121},{"id":"2104.06001","title":"Gender Bias in Machine Translation","created":"2021-04-13","cited_by_count":117},{"id":"1908.09369","title":"On Measuring and Mitigating Biased Inferences of Word Embeddings","created":"2019-08-25","cited_by_count":112},{"id":"2005.00268","title":"Towards Controllable Biases in Language Generation","created":"2020-05-01","cited_by_count":112},{"id":"1909.00871","title":"It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution","created":"2019-09-02","cited_by_count":103},{"id":"1909.05088","title":"Getting Gender Right in Neural Machine Translation","created":"2019-09-11","cited_by_count":98},{"id":"1904.08783","title":"Evaluating the Underlying Gender Bias in Contextualized Word Embeddings","created":"2019-04-18","cited_by_count":94},{"id":"1810.03611","title":"Understanding the Origins of Bias in Word Embeddings","created":"2018-10-08","cited_by_count":91},{"id":"1910.10486","title":"Does Gender Matter? Towards Fairness in Dialogue Systems","created":"2019-10-16","cited_by_count":89},{"id":"2004.04498","title":"Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem","created":"2020-04-09","cited_by_count":86},{"id":"1910.13913","title":"Toward Gender-Inclusive Coreference Resolution","created":"2019-10-30","cited_by_count":84},{"id":"2010.14534","title":"Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias","created":"2020-10-27","cited_by_count":82},{"id":"2005.00614","title":"Multi-Dimensional Gender Bias Classification","created":"2020-05-01","cited_by_count":80},{"id":"2003.11515","title":"Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings","created":"2020-03-11","cited_by_count":79},{"id":"2102.00287","title":"Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation","created":"2021-01-30","cited_by_count":70},{"id":"1901.03116","title":"Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques","created":"2019-01-10","cited_by_count":65},{"id":"1901.07656","title":"Attenuating Bias in Word Vectors","created":"2019-01-23","cited_by_count":64},{"id":"1906.00742","title":"Gender-preserving Debiasing for Pre-trained Word Embeddings","created":"2019-06-03","cited_by_count":61},{"id":"2002.08911","title":"Measuring Social Biases in Grounded Vision and Language Embeddings","created":"2020-02-20","cited_by_count":61},{"id":"1905.11684","title":"On Measuring Gender Bias in Translation of Gender-neutral Pronouns","created":"2019-05-28","cited_by_count":60},{"id":"1908.06361","title":"Understanding Undesirable Word Embedding Associations","created":"2019-08-18","cited_by_count":59},{"id":"1905.12801","title":"Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function","created":"2019-05-30","cited_by_count":57},{"id":"2005.00699","title":"Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer","created":"2020-05-02","cited_by_count":55},{"id":"1904.05233","title":"What's in a Name? Reducing Bias in Bios without Access to Protected Attributes","created":"2019-04-10","cited_by_count":51},{"id":"2002.10361","title":"Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition","created":"2020-02-24","cited_by_count":43},{"id":"2004.14088","title":"Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting","created":"2020-04-29","cited_by_count":41},{"id":"1909.02224","title":"Examining Gender Bias in Languages with Grammatical Gender","created":"2019-09-05","cited_by_count":40},{"id":"2009.05021","title":"Investigating Gender Bias in BERT","created":"2020-09-10","cited_by_count":40},{"id":"1909.06092","title":"A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces","created":"2019-09-13","cited_by_count":36},{"id":"1912.00578","title":"Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models","created":"2019-12-02","cited_by_count":36},{"id":"2005.00965","title":"Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation","created":"2020-05-03","cited_by_count":36},{"id":"1904.11783","title":"Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors","created":"2019-04-26","cited_by_count":31},{"id":"1812.08769","title":"What are the biases in my word embedding?","created":"2018-12-20","cited_by_count":30},{"id":"1910.10872","title":"Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition","created":"2019-10-24","cited_by_count":28},{"id":"2004.14065","title":"Automatically Identifying Gender Issues in Machine Translation using Perturbations","created":"2020-04-29","cited_by_count":28},{"id":"1905.09866","title":"Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor","created":"2019-05-23","cited_by_count":27},{"id":"2005.06251","title":"Mitigating Gender Bias Amplification in Distribution by Posterior Regularization","created":"2020-05-13","cited_by_count":27},{"id":"2105.05541","title":"Evaluating Gender Bias in Natural Language Inference","created":"2021-05-12","cited_by_count":27},{"id":"2101.09525","title":"Dictionary-based Debiasing of Pre-trained Word Embeddings","created":"2021-01-23","cited_by_count":24},{"id":"1911.03642","title":"Towards Understanding Gender Bias in Relation Extraction","created":"2019-11-09","cited_by_count":23},{"id":"2005.05921","title":"Intersectional Bias in Hate Speech and Abusive Language Datasets","created":"2020-05-12","cited_by_count":22},{"id":"1909.08975","title":"Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment","created":"2019-09-19","cited_by_count":20},{"id":"1906.04760","title":"Unsupervised Discovery of Gendered Language through Latent-Variable Modeling","created":"2019-06-11","cited_by_count":19},{"id":"1906.10256","title":"Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis","created":"2019-06-24","cited_by_count":19},{"id":"1908.02810","title":"Debiasing Embeddings for Reduced Gender Bias in Text Classification","created":"2019-08-07","cited_by_count":19},{"id":"2106.08680","title":"Evaluating Gender Bias in Hindi-English Machine Translation","created":"2021-06-16","cited_by_count":19},{"id":"1906.05993","title":"Conceptor Debiasing of Word Representations Evaluated on WEAT","created":"2019-06-14","cited_by_count":18},{"id":"1912.02761","title":"Measuring Social Bias in Knowledge Graph Embeddings","created":"2019-12-05","cited_by_count":17},{"id":"2008.01548","title":"Defining and Evaluating Fair Natural Language Generation","created":"2020-07-28","cited_by_count":17},{"id":"1906.01161","title":"Resolving Gendered Ambiguous Pronouns with BERT","created":"2019-06-03","cited_by_count":16},{"id":"2106.02578","title":"Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants","created":"2021-06-04","cited_by_count":16},{"id":"1606.06121","title":"Quantifying and Reducing Stereotypes in Word Embeddings","created":"2016-06-20","cited_by_count":15},{"id":"2005.06625","title":"Cyberbullying Detection with Fairness Constraints","created":"2020-05-09","cited_by_count":15},{"id":"2011.00244","title":"Evaluating Bias In Dutch Word Embeddings","created":"2020-10-31","cited_by_count":15},{"id":"2004.12332","title":"Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds","created":"2020-04-26","cited_by_count":13},{"id":"1911.10787","title":"A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations","created":"2019-11-25","cited_by_count":11},{"id":"2012.13176","title":"Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters","created":"2020-12-24","cited_by_count":11},{"id":"2107.05987","title":"Generating Gender Augmented Data for NLP","created":"2021-07-13","cited_by_count":11},{"id":"1909.00098","title":"(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas","created":"2019-08-31","cited_by_count":10},{"id":"2004.03133","title":"Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation","created":"2020-04-07","cited_by_count":10},{"id":"2005.08864","title":"Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings","created":"2020-05-18","cited_by_count":9},{"id":"2010.02867","title":"LOGAN: Local Group Bias Detection by Clustering","created":"2020-10-06","cited_by_count":9},{"id":"2104.05232","title":"Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation","created":"2021-04-12","cited_by_count":9},{"id":"2106.00181","title":"Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives","created":"2021-06-01","cited_by_count":7},{"id":"1806.06301","title":"Biased Embeddings from Wild Data: Measuring, Understanding and Removing","created":"2018-06-16","cited_by_count":6}],"next_cursor":"100"}