FairCanary: Rapid Continuous Explainable Fairness

13 June 2021

Papers citing "FairCanary: Rapid Continuous Explainable Fairness"

40 / 40 papers shown

Title
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models David Nigenda Zohar Karnin Muhammad Bilal Zafar Raghu Ramesha Alan Tan Michele Donini K. Kenthapadi VLM 24 41 0 26 Nov 2021
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs Solon Barocas Anhong Guo Ece Kamar J. Krones Meredith Ringel Morris Jennifer Wortman Vaughan Duncan Wadsworth Hanna M. Wallach 57 77 0 10 Mar 2021
Unified Shapley Framework to Explain Prediction Drift Aalok Shanbhag A. Ghosh Josh Rubin FAtt FedML AI4TS 35 3 0 15 Feb 2021
Re-imagining Algorithmic Fairness in India and Beyond Nithya Sambasivan Erin Arnesen Ben Hutchinson Tulsee Doshi Vinodkumar Prabhakaran FaML 63 182 0 25 Jan 2021
Characterizing Intersectional Group Fairness with Worst-Case Comparisons A. Ghosh Lea Genuit Mary Reagan FaML 110 53 0 05 Jan 2021
Wasserstein-based fairness interpretability framework for machine learning models A. Miroshnikov Konstandinos Kotsiopoulos Ryan Franks Arjun Ravi Kannan FAtt 39 15 0 06 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 58 172 0 08 Oct 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 78 464 0 31 Jul 2020
Achieving Fairness via Post-Processing in Web-Scale Recommender Systems Preetam Nandy Cyrus DiCiccio Divya Venugopalan Heloise Logan Kinjal Basu N. Karoui FaML 46 30 0 19 Jun 2020
Problems with Shapley-value-based explanations as feature importance measures Indra Elizabeth Kumar Suresh Venkatasubramanian C. Scheidegger Sorelle A. Friedler TDI FAtt 74 362 0 25 Feb 2020
Fairness-Aware Neural Réyni Minimization for Continuous Features Vincent Grari Boris Ruf Sylvain Lamprier Marcin Detyniecki 35 27 0 12 Nov 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 534 4,323 0 23 Aug 2019
Automatic Model Monitoring for Data Streams Fábio Pinto Marco O. P. Sampaio P. Bizarro AI4TS 23 20 0 12 Aug 2019
Wasserstein Fair Classification Ray Jiang Aldo Pacchiano T. Stepleton Heinrich Jiang Silvia Chiappa 62 177 0 28 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 79 491 0 09 Jul 2019
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding Niki Kilbertus Philip J. Ball Matt J. Kusner Adrian Weller Ricardo M. A. Silva 55 58 0 01 Jul 2019
FlipTest: Fairness Testing via Optimal Transport Emily Black Samuel Yeom Matt Fredrikson 124 96 0 21 Jun 2019
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices Manish Raghavan Solon Barocas Jon M. Kleinberg K. Levy MLAU FaML 60 521 0 21 Jun 2019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms Alekh Agarwal Miroslav Dudík Zhiwei Steven Wu FaML 44 247 0 30 May 2019
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search S. Geyik Stuart Ambler K. Kenthapadi 88 381 0 30 Apr 2019
Stable and Fair Classification Lingxiao Huang Nisheeth K. Vishnoi FaML 93 71 0 21 Feb 2019
ML Health: Fitness Tracking for Production Models Sindhu Ghanta Sriram Subramanian L. Khermosh S. Sundararaman Harshil Shah Y. Goldberg D. Roselli Nisha Talagala 26 8 0 07 Feb 2019
50 Years of Test (Un)fairness: Lessons for Machine Learning Ben Hutchinson Margaret Mitchell AILaw FaML 56 358 0 25 Nov 2018
Understanding the Origins of Bias in Word Embeddings Marc-Etienne Brunet Colleen Alkalay-Houlihan Ashton Anderson R. Zemel FaML 69 200 0 08 Oct 2018
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias Rachel K. E. Bellamy Kuntal Dey Michael Hind Samuel C. Hoffman Stephanie Houde ... Diptikalyan Saha P. Sattigeri Moninder Singh Kush R. Varshney Yunfeng Zhang FaML SyDa 96 806 0 03 Oct 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 95 681 0 28 Jun 2018
Causal Reasoning for Algorithmic Fairness Joshua R. Loftus Chris Russell Matt J. Kusner Ricardo M. A. Silva FaML CML 59 125 0 15 May 2018
Delayed Impact of Fair Machine Learning Lydia T. Liu Sarah Dean Esther Rolf Max Simchowitz Moritz Hardt FaML 82 477 0 12 Mar 2018
Fair Forests: Regularized Tree Induction to Minimize Model Bias Edward Raff Jared Sylvester S. Mills FaML 39 69 0 21 Dec 2017
A Convex Framework for Fair Regression R. Berk Hoda Heidari S. Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel Aaron Roth FaML 107 342 0 07 Jun 2017
Fair Inference On Outcomes Razieh Nabi I. Shpitser FaML 49 351 0 29 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 863 21,760 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 172 3,865 0 10 Apr 2017
Counterfactual Fairness Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo M. A. Silva FaML 197 1,576 0 20 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 175 5,968 0 04 Mar 2017
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 194 4,301 0 07 Oct 2016
Inherent Trade-Offs in the Fair Determination of Risk Scores Jon M. Kleinberg S. Mullainathan Manish Raghavan FaML 99 1,767 0 19 Sep 2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi Kai-Wei Chang James Zou Venkatesh Saligrama Adam Kalai CVBM FaML 92 3,127 0 21 Jul 2016
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller Wojciech Samek FAtt 66 460 0 04 Apr 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 917 16,891 0 16 Feb 2016