Fetching the paper…
Reading the bibliography…
As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gender and racial biases) has come to the fore of the public's attention.
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel · 2011
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Machine Bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Earlier work this paper cites.
Quantifying Distributional Model Risk via Optimal Transport
Jose Blanchet and Karthyek R. A. Murthy · 2016
Earlier work this paper cites.
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Earlier work this paper cites.
How we analyzed the compas recidivism algorithm
Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin · 2016
Earlier work this paper cites.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2017
Earlier work this paper cites.
UCI machine learning repository
Dheeru Dua, Casey Graff, et al · 2017
Cited alongside, same era.
On conditional parity as a notion of non-discrimination in machine learning
Ya’acov Ritov, Yuekai Sun, and Ruofei Zhao · 2017
Cited alongside, same era.
A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Cited alongside, same era.
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang · 2018
Cited alongside, same era.
Amazon scraps secret AI recruiting tool that showed bias against women
Metric Learning for Individual Fairness
Christina Ilvento · 2019
Later among the works it cites.
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes
Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, and Adam Tauman Kalai · 2019
Later among the works it cites.
Apple Card Investigated After Gender Discrimination Complaints
Neil Vigdor · 2019
Later among the works it cites.
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti, Krishna P. Gummadi, and Adrian Weller · 2019
Later among the works it cites.
Two simple ways to learn individual fairness metrics from data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jeffrey Dastin · 2018
Cited alongside, same era.
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Cited alongside, same era.
Scientific Computing: An Introductory Survey, Revised Second Edition
Michael T Heath · 2018
Cited alongside, same era.
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2018
Cited alongside, same era.
Later among the works it cites.
Auditing ML Models for Individual Bias and Unfairness
Songkai Xue, Mikhail Yurochkin, and Yuekai Sun · 2020
Later among the works it cites.
Training individually fair ML models with sensitive subspace robustness
Mikhail Yurochkin, Amanda Bower, and Yuekai Sun · 2020
Later among the works it cites.