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The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized.
A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai , Vol. 14. Montreal, Canada, 1137–1145
Ron Kohavi et al · 1995
Earlier work this paper cites.
The elements of statistical learning: data mining, inference and prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman, and James Franklin. 2005 · 2005
Earlier work this paper cites.
Professional Java development with the Spring framework
Rod Johnson, Juergen Hoeller, Alef Arendsen, and R Thomas. 2009 · 2009
Earlier work this paper cites.
Transaction Processing Performance Council (TPC): State of the Council 2010, Vol. 6417. 1–9
Raghu Nambiar, Nicholas Wakou, Forrest Carman, and Michael Majdalany. 2010 · 2010
Earlier work this paper cites.
Economic Impact Assessment of NIST’s Text REtrieval Conference (TREC) Program
Gregory Tassey. 2010 · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 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.
Decision theory for discrimination-aware classification. In 2012 IEEE 12th International Conference on Data Mining . IEEE, 924–929
Faisal Kamiran, Asim Karim, and Xiangliang Zhang. 2012 · 2012
Earlier work this paper cites.
OpenML: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo. 2014 · 2014
Earlier work this paper cites.
Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015 . 259–268
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015 · 2015
Earlier work this paper cites.
A comparison of six methods for missing data imputation
Peter Schmitt, Jonas Mandel, and Mickael Guedj. 2015 · 2015
Earlier work this paper cites.
Hidden technical debt in machine learning systems. In Advances in neural information processing systems . 2503–2511
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015 · 2015
Earlier work this paper cites.
On the (im)possibility of fairness
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016 · 2016
Earlier work this paper cites.
Model selection management systems: The next frontier of advanced analytics
Arun Kumar, Robert McCann, Jeffrey Naughton, and Jignesh M Patel. 2016 · 2016
Earlier work this paper cites.
M odel DB: a system for machine learning model management. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics . ACM, 14
Manasi Vartak, Harihar Subramanyam, Wei-En Lee, Srinidhi Viswanathan, Saadiyah Husnoo, Samuel Madden, and Matei Zaharia. 2016 · 2016
Cited alongside, same era.
Tfx: A tensorflow-based production-scale machine learning platform. In KDD . 1387–1395
Denis Baylor, Eric Breck, Heng-Tze Cheng, Noah Fiedel, Chuan Yu Foo, Zakaria Haque, Salem Haykal, Mustafa Ispir, Vihan Jain, Levent Koc, et al · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
Cited alongside, same era.
Fairness testing: testing software for discrimination. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017, Paderborn, Germany, September 4-8, 2017 . 498–510
Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017 · 2017
Cited alongside, same era.
IEEE P7003™ standard for algorithmic bias considerations: work in progress paper. In Proceedings of the International Workshop on Software Fairness, FairWare@ICSE 2018, Gothenburg, Sweden, May 29, 2018 . 38–41
Ansgar R. Koene, Liz Dowthwaite, and Suchana Seth. 2018 · 2018
Later among the works it cites.
21 fairness definitions and their politics
Arvind Narayanan. 2018 · 2018
Later among the works it cites.
Data lifecycle challenges in production machine learning: a survey
Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich. 2018 · 2018
Later among the works it cites.
On Challenges in Machine Learning Model Management
Sebastian Schelter, Felix Biessmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas, Manasi Vartak, Samuel Madden, Hui Miao, Amol Deshpande, et al · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society . ACM, 335–340
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
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Joost Kappelhof. 2017 · 2017
Cited alongside, same era.
It’s Not the Algorithm, It’s the Data
Keith Kirkpatrick. 2017 · 2017
Cited alongside, same era.
Playing with the Data: What Legal Scholars Should Learn about Machine Learning
David Lehr and Paul Ohm. 2017 · 2017
Cited alongside, same era.
Deepxplore: Automated whitebox testing of deep learning systems. In SOSP . 1–18
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017 · 2017
Cited alongside, same era.
On fairness and calibration. In Advances in Neural Information Processing Systems . 5680–5689
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017 · 2017
Cited alongside, same era.
Automatically tracking metadata and provenance of machine learning experiments
Sebastian Schelter, Joos-Hendrik Boese, Johannes Kirschnick, Thoralf Klein, and Stephan Seufert. 2017 · 2017
Cited alongside, same era.
Fides: Towards a Platform for Responsible Data Science. In Proceedings of the 29th International Conference on Scientific and Statistical Database Management, Chicago, IL, USA, June 27-29, 2017 . 26:1–26:6
Julia Stoyanovich, Bill Howe, Serge Abiteboul, Gerome Miklau, Arnaud Sahuguet, and Gerhard Weikum. 2017 · 2017
Cited alongside, same era.
FairTest: Discovering Unwarranted Associations in Data-Driven Applications. In 2017 IEEE European Symposium on Security and Privacy, EuroS&P 2017, Paris, France, April 26-28, 2017 . 401–416
Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, and Huang Lin. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Fairness-Aware Programming. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, Atlanta, GA, USA, January 29-31, 2019 . 211–219
Aws Albarghouthi and Samuel Vinitsky. 2019 · 2019
Closest in time.
AI fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2019
Closest in time.
Slice finder: Automated data slicing for model validation. In ICDE . 1550–1553
Yeounoh Chung, Tim Kraska, Neoklis Polyzotis, Ki Hyun Tae, and Steven Euijong Whang. 2019 · 2019
Closest in time.
A comparative study of fairness-enhancing interventions in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, Atlanta, GA, USA, January 29-31, 2019 . 329–338
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth. 2019 · 2019
Closest in time.
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, and Hanna Wallach. 2019 · 2019
Closest in time.
Stable and Fair Classification. In ICML , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. 2879–2890
Lingxiao Huang and Nisheeth Vishnoi. 2019 · 2019
Closest in time.
Interventional fairness: Causal database repair for algorithmic fairness. In SIGMOD . 793–810
Babak Salimi, Luke Rodriguez, Bill Howe, and Dan Suciu. 2019 · 2019
Closest in time.
Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . ACM, 2017–2025
Felix Biessmann, David Salinas, Sebastian Schelter, Philipp Schmidt, and Dustin Lange. 2018 · 2025
Closest in time.