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Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems.
On the Legal Compatibility of Fairness Definitions
Alice Xiang and Inioluwa Deborah Raji · 1912
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Matplotlib: A 2D graphics environment
J. D. Hunter · 2007
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Data Structures for Statistical Computing in Python
Wes McKinney · 2010
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When the implication is not to design (technology)
Eric PS Baumer and M Six Silberman · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The Hidden Biases in Big Data
Kate Crawford · 2013
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Techniques for discrimination-free predictive models
Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy · 2013
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Eric Price, and Nati Srebro · 2016
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Jupyter Notebooks – A Publishing Format for Reproducible Computational Workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, and Carol Willing · 2016
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Cathy O’Neil · 2016
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The Trouble with Bias
Kate Crawford · 2017
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A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Cited alongside, same era.
Artificial Unintelligence: How Computers Misunderstand the World
Meredith Broussard · 2018
Cited alongside, same era.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Joy Buolamwini and Timnit Gebru · 2018
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Algorithms of Oppression: How Search Engines Reinforce Racism
Safiya Umoja Noble · 2018
Cited alongside, same era.
Mitigating Unwanted Biases with Adversarial Learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Cited alongside, same era.
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu · 2019
Cited alongside, same era.
Fairness and Abstraction in Sociotechnical Systems
Andrew D Selbst, danah boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi · 2019
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Fairlearn: A Toolkit for Assessing and Improving Fairness in AI
Sarah Bird, Miroslav Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker · 2020
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Language (technology) is power: A critical survey of” bias” in nlp
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
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Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W. Duncan Wadsworth, and Hanna Wallach · 2021
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Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts, 2021
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Fairness and Machine Learning: Limitations and Opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Cited alongside, same era.
Race After Technology: Abolitionist Tools for the New Jim Code
Ruha Benjamin · 2019
Cited alongside, same era.
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miroslav Dudík, and Hanna Wallach · 2019
Cited alongside, same era.
Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
European Commission (EC) · 2021
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Fairness in AI Systems: From Social Context to Practice using Fairlearn
Triveni Gandhi, Manojit Nandi, Miroslav Dudík, Hanna Wallach, Michael Madaio, Hilde Weerts, Adrin Jalali, and Lisa Ibañez · 2021
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The contestation of tech ethics: A sociotechnical approach to technology ethics in practice
Ben Green · 2021
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Measurement and Fairness
Abigail Z. Jacobs and Hanna Wallach · 2021
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The Landscape and Gaps in Open Source Fairness Toolkits
Michelle Seng Ah Lee and Jat Singh · 2021
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A Blueprint for an AI Bill of Rights, 2022
White House Office of Science and Technology Policy (OSTP) · 2022
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Elizabeth Anne Watkins, Michael McKenna, and Jiahao Chen · 2022
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