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

12 December 2019

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

7 / 7 papers shown

Title
Toward an Evaluation Science for Generative AI Systems Laura Weidinger Deb Raji Hanna M. Wallach Margaret Mitchell Angelina Wang Olawale Salaudeen Rishi Bommasani Sayash Kapoor Deep Ganguli Sanmi Koyejo EGVM ELM 65 4 0 07 Mar 2025
What's documented in AI? Systematic Analysis of 32K AI Model Cards Weixin Liang Nazneen Rajani Xinyu Yang Ezinwanne Ozoani Eric Wu Yiqun Chen D. Smith James Y. Zou 33 15 0 07 Feb 2024
The Fallacy of AI Functionality Inioluwa Deborah Raji Indra Elizabeth Kumar Aaron Horowitz Andrew D. Selbst 15 179 0 20 Jun 2022
Evaluating a Methodology for Increasing AI Transparency: A Case Study David Piorkowski John T. Richards Michael Hind 35 5 0 24 Jan 2022
The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence Kasia Chmielinski S. Newman Matt Taylor Joshua Joseph Kemi Thomas Jessica Yurkofsky Yue Qiu 25 51 0 10 Jan 2022
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 117 355 0 04 Oct 2021
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 192 742 0 13 Dec 2018