ABOUT ML: Annotation and Benchmarking on Understanding and Transparency
of Machine Learning Lifecycles

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ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles

12 December 2019

Inioluwa Deborah Raji

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Papers citing "ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles"

9 / 9 papers shown

Title
Artificial Intelligence: the global landscape of ethics guidelines Anna Jobin M. Ienca E. Vayena 81 1,637 0 24 Jun 2019
Principles alone cannot guarantee ethical AI Brent Mittelstadt 48 834 0 16 Jun 2019
Towards Standardization of Data Licenses: The Montreal Data License Misha Benjamin P. Gagnon Negar Rostamzadeh C. Pal Yoshua Bengio Alex Shee 38 38 0 21 Mar 2019
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 245 767 0 13 Dec 2018
Linking Artificial Intelligence Principles Yi Zeng Enmeng Lu Cunqing Huangfu 46 77 0 12 Dec 2018
Model Cards for Model Reporting Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru 127 1,903 0 05 Oct 2018
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity Matthew Arnold Rachel K. E. Bellamy Michael Hind Stephanie Houde S. Mehta ... Darrell Reimer Alexandra Olteanu David Piorkowski Jason Tsay Kush R. Varshney HILM 47 137 0 22 Aug 2018
Datasheets for Datasets Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer Wortman Vaughan Hanna M. Wallach Hal Daumé Kate Crawford 266 2,194 0 23 Mar 2018
Logics and practices of transparency and opacity in real-world applications of public sector machine learning Michael Veale 30 11 0 19 Jun 2017

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