Review of Mathematical frameworks for Fairness in Machine Learning

26 May 2020

Papers citing "Review of Mathematical frameworks for Fairness in Machine Learning"

50 / 50 papers shown

Title
Accounting for Model Uncertainty in Algorithmic Discrimination Junaid Ali Adish Singla Krishna P. Gummadi FaML 36 21 0 10 May 2021
Fast Fair Regression via Efficient Approximations of Mutual Information D. Steinberg Alistair Reid S. O'Callaghan Finnian Lattimore L. McCalman Tibério S. Caetano FaML 19 16 0 14 Feb 2020
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness Zhu Li Adrián Pérez-Suay Gustau Camps-Valls Dino Sejdinovic FaML 69 21 0 11 Nov 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 465 4,285 0 23 Aug 2019
Wasserstein Fair Classification Ray Jiang Aldo Pacchiano T. Stepleton Heinrich Jiang Silvia Chiappa 47 176 0 28 Jul 2019
FlipTest: Fairness Testing via Optimal Transport Emily Black Samuel Yeom Matt Fredrikson 115 96 0 21 Jun 2019
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification Evgenii Chzhen Christophe Denis Mohamed Hebiri L. Oneto Massimiliano Pontil FaML 92 85 0 12 Jun 2019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms Alekh Agarwal Miroslav Dudík Zhiwei Steven Wu FaML 42 246 0 30 May 2019
Learning Controllable Fair Representations Jiaming Song Pratyusha Kalluri Aditya Grover Shengjia Zhao Stefano Ermon FaML 28 178 0 11 Dec 2018
Taking Advantage of Multitask Learning for Fair Classification L. Oneto Michele Donini Amon Elders Massimiliano Pontil FaML 52 60 0 19 Oct 2018
Active Fairness in Algorithmic Decision Making Alejandro Noriega-Campero Michiel A. Bakker Bernardo Garcia-Bulle Alex Pentland FaML 37 85 0 28 Sep 2018
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices Till Speicher Hoda Heidari Nina Grgic-Hlaca Krishna P. Gummadi Adish Singla Adrian Weller Muhammad Bilal Zafar FaML 35 261 0 02 Jul 2018
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints Andrew Cotter Maya R. Gupta Heinrich Jiang Nathan Srebro Karthik Sridharan S. Wang Blake E. Woodworth Seungil You FaML 16 105 0 29 Jun 2018
Obtaining fairness using optimal transport theory E. del Barrio Fabrice Gamboa Paula Gordaliza Jean-Michel Loubes FaML 66 181 0 08 Jun 2018
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification Michael P. Kim Amirata Ghorbani James Zou MLAU 149 337 0 31 May 2018
Causal Reasoning for Algorithmic Fairness Joshua R. Loftus Chris Russell Matt J. Kusner Ricardo M. A. Silva FaML CML 42 125 0 15 May 2018
Probably Approximately Metric-Fair Learning G. Rothblum G. Yona FaML FedML 38 85 0 08 Mar 2018
A Reductions Approach to Fair Classification Alekh Agarwal A. Beygelzimer Miroslav Dudík John Langford Hanna M. Wallach FaML 92 1,094 0 06 Mar 2018
Empirical Risk Minimization under Fairness Constraints Michele Donini L. Oneto Shai Ben-David John Shawe-Taylor Massimiliano Pontil FaML 65 443 0 23 Feb 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 294 678 0 17 Feb 2018
Fair Clustering Through Fairlets Flavio Chierichetti Ravi Kumar Silvio Lattanzi Sergei Vassilvitskii FaML 37 430 0 15 Feb 2018
Mitigating Unwanted Biases with Adversarial Learning B. Zhang Blake Lemoine Margaret Mitchell FaML 77 1,373 0 22 Jan 2018
Fairness in Supervised Learning: An Information Theoretic Approach AmirEmad Ghassami S. Khodadadian Negar Kiyavash FaML 32 48 0 13 Jan 2018
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport Meike Zehlike P. Hacker Emil Wiedemann 38 19 0 21 Dec 2017
Calibration for the (Computationally-Identifiable) Masses Úrsula Hébert-Johnson Michael P. Kim Omer Reingold G. Rothblum FaML 36 87 0 22 Nov 2017
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer David Madras T. Pitassi R. Zemel FaML 69 220 0 17 Nov 2017
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns Seth Neel Aaron Roth Zhiwei Steven Wu FaML 90 775 0 14 Nov 2017
Fair Kernel Learning Adrián Pérez-Suay Valero Laparra Gonzalo Mateo-García Jordi Munoz-Marí L. Gómez-Chova Gustau Camps-Valls FaML 55 84 0 16 Oct 2017
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations Alex Beutel Jilin Chen Zhe Zhao Ed H. Chi FaML 64 442 0 01 Jul 2017
From Parity to Preference-based Notions of Fairness in Classification Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi Adrian Weller FaML 49 209 0 30 Jun 2017
Avoiding Discrimination through Causal Reasoning Niki Kilbertus Mateo Rojas-Carulla Giambattista Parascandolo Moritz Hardt Dominik Janzing Bernhard Schölkopf FaML CML 81 579 0 08 Jun 2017
A Convex Framework for Fair Regression R. Berk Hoda Heidari S. Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel Aaron Roth FaML 95 342 0 07 Jun 2017
Fair Inference On Outcomes Razieh Nabi I. Shpitser FaML 26 349 0 29 May 2017
Central Limit Theorem for empirical transportation cost in general dimension E. del Barrio Jean-Michel Loubes OT 31 108 0 03 May 2017
Fairness in Criminal Justice Risk Assessments: The State of the Art R. Berk Hoda Heidari S. Jabbari Michael Kearns Aaron Roth 30 990 0 27 Mar 2017
Counterfactual Fairness Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo M. A. Silva FaML 176 1,566 0 20 Mar 2017
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 96 1,204 0 26 Oct 2016
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 280 2,098 0 24 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 80 4,276 0 07 Oct 2016
Inherent Trade-Offs in the Fair Determination of Risk Scores Jon M. Kleinberg S. Mullainathan Manish Raghavan FaML 75 1,762 0 19 Sep 2016
Satisfying Real-world Goals with Dataset Constraints Gabriel Goh Andrew Cotter Maya R. Gupta M. Friedlander OffRL 34 215 0 24 Jun 2016
Towards a Neural Statistician Harrison Edwards Amos Storkey BDL 33 428 0 07 Jun 2016
Auditing Black-box Models for Indirect Influence Philip Adler Casey Falk Sorelle A. Friedler Gabriel Rybeck C. Scheidegger Brandon Smith Suresh Venkatasubramanian TDI MLAU 67 288 0 23 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 366 16,765 0 16 Feb 2016
A Confidence-Based Approach for Balancing Fairness and Accuracy Benjamin Fish Jeremy Kun Á. Lelkes FaML 98 248 0 21 Jan 2016
Censoring Representations with an Adversary Harrison Edwards Amos Storkey AAML FaML 41 504 0 18 Nov 2015
Existence and Consistency of Wasserstein Barycenters Thibaut Le Gouic Jean-Michel Loubes 38 130 0 12 Jun 2015
On the relation between accuracy and fairness in binary classification Indrė Žliobaitė FaML 30 196 0 21 May 2015
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 88 1,978 0 11 Dec 2014
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin Razvan Pascanu Çağlar Gülçehre Kyunghyun Cho Surya Ganguli Yoshua Bengio ODL 84 1,379 0 10 Jun 2014