Principal Fairness: Removing Bias via Projections

31 May 2019

Papers citing "Principal Fairness: Removing Bias via Projections"

10 / 10 papers shown

Title
Scalable Fair Clustering A. Backurs Piotr Indyk Krzysztof Onak B. Schieber A. Vakilian Tal Wagner 70 199 0 10 Feb 2019
Fair k-Center Clustering for Data Summarization Matthäus Kleindessner Pranjal Awasthi Jamie Morgenstern 79 163 0 24 Jan 2019
Fair Algorithms for Clustering Suman Kalyan Bera Deeparnab Chakrabarty Nicolas J. Flores Maryam Negahbani FaML FedML 73 237 0 08 Jan 2019
Active Fairness in Algorithmic Decision Making Alejandro Noriega-Campero Michiel A. Bakker Bernardo Garcia-Bulle Alex Pentland FaML 52 85 0 28 Sep 2018
Fair Clustering Through Fairlets Flavio Chierichetti Ravi Kumar Silvio Lattanzi Sergei Vassilvitskii FaML 70 431 0 15 Feb 2018
From Parity to Preference-based Notions of Fairness in Classification Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi Adrian Weller FaML 73 208 0 30 Jun 2017
On the Local Structure of Stable Clustering Instances Vincent Cohen-Addad Chris Schwiegelshohn 172 48 0 29 Jan 2017
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 196 1,206 0 26 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 222 4,312 0 07 Oct 2016
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 194 1,986 0 11 Dec 2014