Fairkit-learn

Author:

Johnson Brittany1,Brun Yuriy2

Affiliation:

1. George Mason University

2. University of Massachusetts Amherst

Funder

Meta

Kosa.ai

Google

National Science Foundation

Publisher

ACM

Reference39 articles.

1. Julius A. Adebayo . 2016. FairML: ToolBox for diagnosing bias in predictive modeling. Ph. D. Dissertation . Massachusetts Institute of Technology . Julius A. Adebayo. 2016. FairML: ToolBox for diagnosing bias in predictive modeling. Ph. D. Dissertation. Massachusetts Institute of Technology.

2. Themis: automatically testing software for discrimination

3. Rachel K. E. Bellamy , Kuntal Dey , Michael Hind , Samuel C. Hoffman , Stephanie Houde , Kalapriya Kannan , Pranay Lohia , Jacquelyn Martino , Sameep Mehta , Aleksandra Mojsilovic , Seema Nagar , Karthikeyan Natesan Ramamurthy , John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018 . AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR 1810.01943 (2018). https://arxiv.org/abs/1810.01943 Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR 1810.01943 (2018). https://arxiv.org/abs/1810.01943

4. Sarah Bird , Miro Dudík , Richard Edgar , Brandon Horn , Roman Lutz , Vanessa Milan , Mehrnoosh Sameki , Hanna Wallach , and Kathleen Walker . 2020 . Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report MSR-TR-2020-32. Microsoft. Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report MSR-TR-2020-32. Microsoft.

5. Software fairness

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1. Crafting Disability Fairness Learning in Data Science: A Student-Centric Pedagogical Approach;Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1;2024-03-07

2. Fairness-aware machine learning engineering: how far are we?;Empirical Software Engineering;2023-11-24

3. A Taxonomy of Machine Learning Fairness Tool Specifications, Features and Workflows;2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC);2023-10-03

4. Seldonian Toolkit: Building Software with Safe and Fair Machine Learning;2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion);2023-05

5. Blindspots in Python and Java APIs Result in Vulnerable Code;ACM Transactions on Software Engineering and Methodology;2023-04-26

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