Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
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[1] S. Shields,et al. Speaking from the Heart: Gender and the Social Meaning of Emotion (review) , 2004 .
[2] K. L. Marsh,et al. Gender, Race, and Speech Style Stereotypes , 2003 .
[3] Bing Liu,et al. Mining and summarizing customer reviews , 2004, KDD.
[4] Franco Turini,et al. Discrimination-aware data mining , 2008, KDD.
[5] Finn Årup Nielsen,et al. A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.
[6] Saif Mohammad,et al. CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..
[7] Latanya Sweeney,et al. Discrimination in online ad delivery , 2013, CACM.
[8] Josep Domingo-Ferrer,et al. A Methodology for Direct and Indirect Discrimination Prevention in Data Mining , 2013, IEEE Transactions on Knowledge and Data Engineering.
[9] Saif Mohammad,et al. Sentiment Analysis of Short Informal Texts , 2014, J. Artif. Intell. Res..
[10] Sanja Fidler,et al. Skip-Thought Vectors , 2015, NIPS.
[11] Dirk Hovy,et al. Demographic Factors Improve Classification Performance , 2015, ACL.
[12] Michael Carl Tschantz,et al. Automated Experiments on Ad Privacy Settings , 2014, Proc. Priv. Enhancing Technol..
[13] Indre Zliobaite,et al. A survey on measuring indirect discrimination in machine learning , 2015, ArXiv.
[14] Maya R. Gupta,et al. Satisfying Real-world Goals with Dataset Constraints , 2016, NIPS.
[15] Dirk Hovy,et al. The Social Impact of Natural Language Processing , 2016, ACL.
[16] Brendan T. O'Connor,et al. Demographic Dialectal Variation in Social Media: A Case Study of African-American English , 2016, EMNLP.
[17] Adam Tauman Kalai,et al. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.
[18] Iyad Rahwan,et al. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm , 2017, EMNLP.
[19] Alexandra Chouldechova,et al. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments , 2016, Big Data.
[20] Margaret Mitchell,et al. Improving Smiling Detection with Race and Gender Diversity , 2017, ArXiv.
[21] Hee Jung Ryu,et al. InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity , 2017 .
[22] Saif Mohammad,et al. WASSA-2017 Shared Task on Emotion Intensity , 2017, WASSA@EMNLP.
[23] Yulia Tsvetkov,et al. Incorporating Dialectal Variability for Socially Equitable Language Identification , 2017, ACL.
[24] Arvind Narayanan,et al. Semantics derived automatically from language corpora contain human-like biases , 2016, Science.
[25] Bernhard Schölkopf,et al. Avoiding Discrimination through Causal Reasoning , 2017, NIPS.
[26] Jieyu Zhao,et al. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods , 2018, NAACL.
[27] Timnit Gebru,et al. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.
[28] Saif Mohammad,et al. SemEval-2018 Task 1: Affect in Tweets , 2018, *SEMEVAL.
[29] Saif Mohammad,et al. Word Affect Intensities , 2017, LREC.
[30] Blake Lemoine,et al. Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.