MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software-Reference-Cited by-同舟云学术

MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software

Published:2022-11-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Author:

Chen Zhenpeng¹,Zhang Jie M.²,Sarro Federica¹,Harman Mark¹

Affiliation:

1. University College London, UK

2. King’s College London, UK

Funder

The ERC Advanced Grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3540250.3549093

Reference83 articles.

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3. 2015. The Mep dataset. https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181 Retrieved on November 25, 2021 2015. The Mep dataset. https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181 Retrieved on November 25, 2021

4. 2016. The Compas dataset. https://github.com/propublica/compas-analysis Retrieved on November 25, 2021 2016. The Compas dataset. https://github.com/propublica/compas-analysis Retrieved on November 25, 2021

5. 2016. Machine bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Retrieved on November 25, 2021 2016. Machine bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Retrieved on November 25, 2021

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