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

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

13 May 2022

Wesley Hanwen Deng

Manish Nagireddy

Zhiwei Steven Wu

Kenneth Holstein

Haiyi Zhu

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

11 / 11 papers shown

Title
The Fall of an Algorithm: Characterizing the Dynamics Toward Abandonment Nari Johnson Sanika Moharana Christina Harrington Nazanin Andalibi Hoda Heidari Motahhare Eslami 26 7 0 21 Apr 2024
Towards a Non-Ideal Methodological Framework for Responsible ML Ramaravind Kommiya Mothilal Shion Guha Syed Ishtiaque Ahmed 40 7 0 20 Jan 2024
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience Q. V. Liao Hariharan Subramonyam Jennifer Wang Jennifer Wortman Vaughan HAI 22 58 0 21 Feb 2023
Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service" Kornel Lewicki M. S. Lee Jennifer Cobbe Jatinder Singh 26 21 0 02 Feb 2023
Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook Nur Yildirim Mahima Pushkarna Nitesh Goyal Martin Wattenberg Fernanda Viégas 34 66 0 28 Jan 2023
Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice Wesley Hanwen Deng B. Guo Alicia DeVrio Hong Shen Motahhare Eslami Kenneth Holstein MLAU 17 58 0 07 Oct 2022
Discovering and Validating AI Errors With Crowdsourced Failure Reports Ángel Alexander Cabrera Abraham J. Druck Jason I. Hong Adam Perer HAI 48 54 0 23 Sep 2021
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects Samir Passi S. Jackson 168 108 0 09 Feb 2020
Data Vision: Learning to See Through Algorithmic Abstraction Samir Passi S. Jackson 135 111 0 09 Feb 2020
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
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016