Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits-Reference-Cited by-同舟云学术

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

Published:2022-06-20 Issue: Volume: Page:
ISSN:
Container-title:2022 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Deng Wesley Hanwen¹,Nagireddy Manish²,Lee Michelle Seng Ah³,Singh Jatinder³,Wu Zhiwei Steven²,Holstein Kenneth²,Zhu Haiyi²

Affiliation:

1. Human-Computer Interaction Institute, Carnegie Mellon University, USA

2. Carnegie Mellon University, USA

3. University of Cambridge, United Kingdom

Funder

Carnegie Mellon University Block Center for Technology and Society Award

Aviva and the UK Engineering and Physical Science Research Council

Jacob Foundation for CERES network

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3531146.3533113

Reference114 articles.

1. 2017. Facets - visualizations for ML datasets.arXiv:1810.01943https://pair-code.github.io/facets/ 2017. Facets - visualizations for ML datasets.arXiv:1810.01943https://pair-code.github.io/facets/

2. 2021. People AI Guidebook. (2021). https://pair.withgoogle.com/guidebook/ 2021. People AI Guidebook. (2021). https://pair.withgoogle.com/guidebook/

3. Martín Abadi and Ashish Agarwal et al.2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org. Martín Abadi and Ashish Agarwal et al.2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.

4. 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.

5. Yongsu Ahn and Yu-Ru Lin . 2019 . Fairsight: Visual analytics for fairness in decision making . IEEE transactions on visualization and computer graphics 26, 1(2019), 1086–1095. Yongsu Ahn and Yu-Ru Lin. 2019. Fairsight: Visual analytics for fairness in decision making. IEEE transactions on visualization and computer graphics 26, 1(2019), 1086–1095.

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