Fetching the paper…
Reading the bibliography…
Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases.
Speaking from the heart: Gender and the social meaning of emotion
Stephanie A. Shields. 2002 · 2002
Earlier work this paper cites.
Gender, race, and speech style stereotypes
Danielle Popp, Roxanne Angela Donovan, Mary Crawford, Kerry L. Marsh, and Melanie Peele. 2003 · 2003
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
Earlier work this paper cites.
Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini. 2008 · 2008
Earlier work this paper cites.
A new ANEW: Evaluation of a word list for sentiment analysis in microblogs
Finn Årup Nielsen. 2011 · 2011
Earlier work this paper cites.
A methodology for direct and indirect discrimination prevention in data mining
Sara Hajian and Josep Domingo-Ferrer. 2013 · 2013
Earlier work this paper cites.
Crowdsourcing a word–emotion association lexicon
Saif M. Mohammad and Peter D. Turney. 2013 · 2013
Earlier work this paper cites.
Discrimination in online ad delivery
Latanya Sweeney. 2013 · 2013
Earlier work this paper cites.
Sentiment analysis of short informal texts
Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. 2014 · 2014
Earlier work this paper cites.
Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta. 2015 · 2015
Earlier work this paper cites.
Demographic factors improve classification performance
Dirk Hovy. 2015 · 2015
Earlier work this paper cites.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Rejecting the gender binary: a vector-space operation
Ben Schmidt. 2015 · 2015
Cited alongside, same era.
A survey on measuring indirect discrimination in machine learning
Indre Zliobaite. 2015 · 2015
Cited alongside, same era.
Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
Cited alongside, same era.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
Cited alongside, same era.
Satisfying real-world goals with dataset constraints
Incorporating dialectal variability for socially equitable language identification
David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017 · 2017
Later among the works it cites.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
Later among the works it cites.
WASSA-2017 shared task on emotion intensity
Saif M. Mohammad and Felipe Bravo-Marquez. 2017 · 2017
Later among the works it cites.
Improving smiling detection with race and gender diversity
Hee Jung Ryu, Margaret Mitchell, and Hartwig Adam. 2017 · 2017
Later among the works it cites.
ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors
Rob Speer. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gabriel Goh, Andrew Cotter, Maya Gupta, and Michael P Friedlander. 2016 · 2016
Cited alongside, same era.
The social impact of natural language processing
Dirk Hovy and Shannon L Spruit. 2016 · 2016
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
Cited alongside, same era.
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017 · 2017
Cited alongside, same era.
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Closest in time.
Word affect intensities
Saif M. Mohammad. 2018 · 2018
Closest in time.
Semeval-2018 Task 1: Affect in tweets
Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018 · 2018
Closest in time.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Closest in time.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
Closest in time.