A snapshot of the frontiers of fairness in machine learning - Concepedia

Concepedia

Abstract

A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias.

References

	Year	Citations
The Algorithmic Foundations of Differential Privacy Cynthia Dwork, Aaron Roth Foundations and Trends® in Theoretical Computer Science	2014	3.9K
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification Joy Buolamwini, Timnit Gebru Gender DisparityGendered PerceptionGender IdentityGender StudiesIntersectionality	2018	3.3K
Fairness through awareness Cynthia Dwork, Moritz Hardt, Toniann Pitassi, EngineeringDiscriminationFairness Through AwarenessSocial StratificationClassification Task	2012	3.3K
The Economics of Discrimination Richard Leonard, Gary S. Becker The American Catholic Sociological Review Race LawDiscriminationRacial PrejudiceLawDiscrimination Law	1957	3K
Semantics derived automatically from language corpora contain human-like biases Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan Science	2017	2.7K
Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, Nathan Srebro arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningSpecified Sensitive AttributeDiscrimination	2016	1.9K
Certifying and Removing Disparate Impact Michael Feldman, Sorelle A. Friedler, John Moeller, Protected ClassDiscriminationLawEducationDiscrimination Law	2015	1.7K
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi, Kai-Wei Chang, James Zou, arXiv (Cornell University) EngineeringMachine LearningCross-lingual RepresentationDebiasing Word EmbeddingsCommunication	2016	1.4K
Algorithmic Decision Making and the Cost of Fairness Sam Corbett‐Davies, Emma Pierson, Avi Feller, Game TheoryAlgorithmic AccountabilityLawCriminal LawRacial Disparities	2017	1.1K
Learning Fair Representations Rich Zemel, Yu Wu, Kevin Swersky,	2013	992

Page 1