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Fairness testing aims at mitigating unintended discrimination in the decision-making process of data-driven AI systems.
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Applied linear regression
Sanford Weisberg · 2005
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A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making
Anthony Finkelstein, Mark Harman, S Afshin Mansouri, Jian Ren, and Yuanyuan Zhang · 2009
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The feature importance ranking measure
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Using the shapley value to analyze algorithm portfolios
Alexandre Fréchette, Lars Kotthoff, Tomasz Michalak, Talal Rahwan, Holger Hoos, and Kevin Leyton-Brown · 2016
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A convex framework for fair regression
R. Berk, H. Heidari, S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, S. Neel, and A. Roth · 2017
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Fairness testing: testing software for discrimination
Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Dbscan revisited, revisited: why and how you should (still) use dbscan
Erich Schubert, Jörg Sander, Martin Ester, Hans Peter Kriegel, and Xiaowei Xu · 2017
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Automated test generation to detect individual discrimination in ai models
Aniya Agarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha · 2018
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Themis: Automatically testing software for discrimination
Rico Angell, Brittany Johnson, Yuriy Brun, and Alexandra Meliou · 2018
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Software fairness
Yuriy Brun and Alexandra Meliou · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Automated directed fairness testing
Machine learning testing: Survey, landscapes and horizons
Jie M Zhang, Mark Harman, Lei Ma, and Yang Liu · 2020
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White-box fairness testing through adversarial sampling
Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, and Ting Dai · 2020
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Fair preprocessing: Towards understanding compositional fairness of data transformers in machine learning pipeline
Sumon Biswas and Hridesh Rajan · 2021
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Bias in machine learning software: Why? how? what to do?
Joymallya Chakraborty, Suvodeep Majumder, and Tim Menzies · 2021
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Did you do your homework? raising awareness on software fairness and discrimination
Max Hort and Federica Sarro · 2021
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Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods
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Sakshi Udeshi, Pryanshu Arora, and Sudipta Chattopadhyay · 2018
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Black box fairness testing of machine learning models
Aniya Aggarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha · 2019
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Investigating the effects of gender bias on github
Nasif Imtiaz, Justin Middleton, Joymallya Chakraborty, Neill Robson, Gina Bai, and Emerson Murphy-Hill · 2019
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Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness
Sumon Biswas and Hridesh Rajan · 2020
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Fairway: A way to build fair ml software
Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu, and Tim Menzies · 2020
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Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2020
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Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen J Giguere, and Yuriy Brun · 2020
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Max Hort, Jie M Zhang, Federica Sarro, and Mark Harman · 2021
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Jump-Starting multivariate time series anomaly detection for online service systems
Minghua Ma, Shenglin Zhang, Junjie Chen, Jim Xu, Haozhe Li, Yongliang Lin, Xiaohui Nie, Bo Zhou, Yong Wang, and Dan Pei · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias
Trusted-AI · 2021
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“ignorance and prejudice” in software fairness
Jie M Zhang and Mark Harman · 2021
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Efficient white-box fairness testing through gradient search
Lingfeng Zhang, Yueling Zhang, and Min Zhang · 2021
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Fairness testing: A comprehensive survey and analysis of trends
Zhenpeng Chen, Jie M Zhang, Max Hort, Federica Sarro, and Mark Harman · 2022
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Bias mitigation for machine learning classifiers: A comprehensive survey
Max Hort, Zhenpeng Chen, Jie M Zhang, Federica Sarro, and Mark Harman · 2022
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Software fairness: An analysis and survey, 2022
Ezekiel Soremekun, Mike Papadakis, Maxime Cordy, and Yves Le Traon · 2022
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Software fairness: An analysis and survey
Ezekiel Soremekun, Mike Papadakis, Maxime Cordy, and Yves Le Traon · 2022
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