Understanding Machine Learning Practitioners' Data Documentation
Perceptions, Needs, Challenges, and Desiderata

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

6 June 2022

Elizabeth B. Marquis

Mihaela Vorvoreanu

Hanna M. Wallach

Papers citing "Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata"

12 / 12 papers shown

Title
Fairness Practices in Industry: A Case Study in Machine Learning Teams Building Recommender Systems Jing Nathan Yan Junxiong Wang Jeffrey M. Rzeszotarski Allison Koenecke FaML 12 0 0 26 May 2025
My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data Hauke Sandhaus Angel Hsing-Chi Hwang Wendy Ju Qian Yang 68 1 0 10 Apr 2025
SPHERE: An Evaluation Card for Human-AI Systems Qianou Ma Dora Zhao Xinran Zhao Chenglei Si Chenyang Yang Ryan Louie Ehud Reiter Diyi Yang Tongshuang Wu ALM 70 1 0 24 Mar 2025
Towards a Non-Ideal Methodological Framework for Responsible ML Ramaravind Kommiya Mothilal Shion Guha Syed Ishtiaque Ahmed 59 7 0 20 Jan 2024
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap Q. V. Liao J. Vaughan 77 160 0 02 Jun 2023
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements Samantha Robertson Zijie J. Wang Dominik Moritz Mary Beth Kery Fred Hohman 72 15 0 12 Apr 2023
Right the docs: Characterising voice dataset documentation practices used in machine learning Kathy Reid Elizabeth T. Williams 37 2 0 19 Mar 2023
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 46 60 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 41 22 0 02 Feb 2023
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects Samir Passi S. Jackson 177 111 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 198 745 0 13 Dec 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 227 2,094 0 24 Oct 2016