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Ethical bias in machine learning models has become a matter of concern in the software engineering community.
Identifying and Correcting Label Bias in Machine Learning
Heinrich Jiang and Ofir Nachum. 2019 · 1901
Earlier work this paper cites.
Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)
Huy Tu, Zhe Yu, and Tim Menzies. 2020a · 1905
Earlier work this paper cites.
UCI:Adult Data Set
1994 · 1994
Earlier work this paper cites.
Unsupervised Word Sense Disambiguation Rivaling Supervised Methods (ACL ’95) . Association for Computational Linguistics, USA, 189–196
David Yarowsky. 1995 · 1995
Earlier work this paper cites.
Combining Labeled and Unlabeled Data with Co-Training (COLT’ 98) . Association for Computing Machinery, New York, NY, USA, 92–100
Avrim Blum and Tom Mitchell. 1998 · 1998
Earlier work this paper cites.
UCI:Statlog (German Credit Data) Data Set
2000 · 2000
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UCI:Heart Disease Data Set
2001 · 2001
Earlier work this paper cites.
Class-Imbalanced Semi-Supervised Learning
Minsung Hyun, Jisoo Jeong, and Nojun Kwak. 2020 · 2002
Earlier work this paper cites.
Learning from Labeled and Unlabeled Data with Label Propagation
Xiaojin Zhu and Zoubin Ghahramani. 2002 · 2002
Earlier work this paper cites.
Learning with Local and Global Consistency. In Advances in Neural Information Processing Systems , S. Thrun, L. Saul, and B. Schölkopf (Eds.), Vol. 16. MIT Press
Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Schölkopf. 2004 · 2003
Earlier work this paper cites.
Semi-Supervised Learning Literature Survey
Xiaojin Zhu. 2006 · 2006
Earlier work this paper cites.
Situation Testing for Employment Discrimination in the United States of America
2007 · 2007
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The Algorithm That Beats Your Bank Manager
2011 · 2011
Earlier work this paper cites.
Semi-Supervised Learning for Imbalanced Sentiment Classification (IJCAI’11) . AAAI Press, 1826–1831
Shoushan Li, Zhongqing Wang, Guodong Zhou, and Sophia Yat Mei Lee. 2011 · 2011
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders. 2012 · 2012
Earlier work this paper cites.
Fairness-Aware Classifier with Prejudice Remover Regularizer. In Machine Learning and Knowledge Discovery in Databases , Peter A. Flach, Tijl De Bie, and Nello Cristianini (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 35–50
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012
Earlier work this paper cites.
Ranking and clustering software cost estimation models through a multiple comparisons algorithm
Nikolaos Mittas and Lefteris Angelis. 2013 · 2013
Earlier work this paper cites.
Student Performance Data Set
2014 · 2014
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Medical Expenditure Panel Survey
2015 · 2015
Earlier work this paper cites.
propublica/compas-analysis
2015 · 2015
Earlier work this paper cites.
Revisiting the impact of classification techniques on the performance of defect prediction models. In 37th ICSE-Volume 1 . IEEE Press, 789–800
Baljinder Ghotra, Shane McIntosh, and Ahmed E Hassan. 2015 · 2015
Earlier work this paper cites.
Medical Expenditure Panel Survey
2016 · 2016
Earlier work this paper cites.
UCI:Default of credit card clients Data Set
2016 · 2016
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
Earlier work this paper cites.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016 · 2016
Cited alongside, same era.
Network Science and Applications
Dapeng Oliver Wu. 2016 · 2016
Cited alongside, same era.
Bank Marketing UCI
2017 · 2017
Cited alongside, same era.
THome Credit Default Risk
2017 · 2017
Cited alongside, same era.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2017 · 2017
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
Cited alongside, same era.
Black Box Fairness Testing of Machine Learning Models (ESEC/FSE 2019) . ACM, New York, NY, USA, 625–635
Aniya Aggarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha. 2019 · 2019
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Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
Joymallya Chakraborty, Tianpei Xia, Fahmid M. Fahid, and Tim Menzies. [n.d.] · 2019
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What Do We Do About the Biases in AI?
Jake Silberg James Manyika and Brittany Presten. 2019 · 2019
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Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia (ICAIL ’19) . Association for Computing Machinery, New York, NY, USA, 83–92
Songül Tolan, Marius Miron, Emilia Gómez, and Carlos Castillo. 2019 · 2019
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Human Bias in Machine Learning
Mark Xiang. 2019 · 2019
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Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. 2017 · 2017
Cited alongside, same era.
The No Free Lunch Theorem (or why you can’t have your cake and eat it)
Leon Fedden. 2017 · 2017
Cited alongside, same era.
Fairness testing: testing software for discrimination
Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017 · 2017
Cited alongside, same era.
Exploiting Reject Option in Classification for Social Discrimination Control
Faisal Kamiran, Sameen Mansha, Asim Karim, and Xiangliang Zhang. 2018 · 2017
Cited alongside, same era.
IEEE standard review — Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. In 2017 IEEE Canada International Humanitarian Technology Conference (IHTC) . 197–201
Kyarash Shahriari and Mana Shahriari. 2017 · 2017
Cited alongside, same era.
Gender and Dialect Bias in YouTube’s Automatic Captions. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing . Association for Computational Linguistics, Valencia, Spain, 53–59
Rachael Tatman. 2017 · 2017
Cited alongside, same era.
TERMINATOR: Better Automated UI Test Case Prioritization (ESEC/FSE 2019) . Association for Computing Machinery, New York, NY, USA, 883–894
Zhe Yu, Fahmid Fahid, Tim Menzies, Gregg Rothermel, Kyle Patrick, and Snehit Cherian. 2019 · 2019
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Improving Vulnerability Inspection Efficiency Using Active Learning
Z. Yu, C. Theisen, L. Williams, and T. Menzies. 2019 · 2019
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HireVue Assessment Tools
2020 · 2020
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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 · 2020
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Fairway: A Way to Build Fair ML Software (ESEC/FSE 2020) . Association for Computing Machinery, New York, NY, USA, 654–665
Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu, and Tim Menzies. 2020a · 2020
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Fairness Measures for Machine Learning in Finance
Sanjiv Das and Michele Donini. 2020 · 2020
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Addressing issues of fairness and bias in AI
Ted Simons. 2020 · 2020
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Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)
Huy Tu, Zhe Yu, and Tim Menzies. 2020b · 2020
Later among the works it cites.
Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes (CIKM ’20) . Association for Computing Machinery, New York, NY, USA, 1715–1724
Shen Yan, Hsien-te Kao, and Emilio Ferrara. 2020 · 2020
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Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
Tao Zhang, tianqing zhu, Jing Li, Mengde Han, Wanlei Zhou, and Philip Yu. 2020b · 2020
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Effect of varying threshold for self-training
2021a · 2021
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Fairlearn
2021 · 2021
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Google Cloud Pricing
2021 · 2021
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Log-linear Models and Conditional Random Fields
2021 · 2021
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The most costly thing in Machine Learning Algorithms is labeling
2021 · 2021
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Probability calibration
2021b · 2021
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Bias in Machine Learning Software: Why? How? What to Do? (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 429–440
Joymallya Chakraborty, Suvodeep Majumder, and Tim Menzies. 2021 · 2021
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FRUGAL: Unlocking SSL for Software Analytics
Huy Tu and Tim Menzies. 2021 · 2021
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