Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?

16 September 2021

Papers citing "Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?"

11 / 11 papers shown

Title
The Human Factor in AI Red Teaming: Perspectives from Social and Collaborative Computing Alice Qian Zhang Ryland Shaw Jacy Reese Anthis Ashlee Milton Emily Tseng ... Ram Shankar Siva Kumar Julian Posada Benjamin Shestakofsky Sarah T. Roberts Mary L. Gray AAML 20 5 0 10 Jul 2024
Gender Bias Detection in Court Decisions: A Brazilian Case Study Raysa Benatti F. Severi Sandra Avila Esther Luna Colombini 31 1 0 01 Jun 2024
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use Anna Kawakami Amanda Coston Hoda Heidari Kenneth Holstein Haiyi Zhu 44 8 0 21 May 2024
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology Emma Harvey Hauke Sandhaus Abigail Z. Jacobs Emanuel Moss Mona Sloane 11 3 0 19 Jan 2024
Making Data Work Count Srravya Chandhiramowuli Alex S. Taylor S. Heitlinger Ding Wang 26 7 0 29 Nov 2023
The Ethics of AI Value Chains Blair Attard-Frost D. Widder 22 1 0 31 Jul 2023
Patterns of gender-specializing query reformulation Amifa Raj Bhaskar Mitra Nick Craswell Michael D. Ekstrand 6 1 0 25 Apr 2023
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits Wesley Hanwen Deng Manish Nagireddy M. S. Lee Jatinder Singh Zhiwei Steven Wu Kenneth Holstein Haiyi Zhu 31 87 0 13 May 2022
The Tragedy of the AI Commons Travis LaCroix Aydin Mohseni 9 11 0 09 Jun 2020
Data Vision: Learning to See Through Algorithmic Abstraction Samir Passi S. Jackson 131 111 0 09 Feb 2020
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets Mor Geva Yoav Goldberg Jonathan Berant 237 319 0 21 Aug 2019