Keeping Community in the Loop: Understanding Wikipedia Stakeholder
Values for Machine Learning-Based Systems

Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems

14 January 2020

Anjali Srivastava

Aaron L Halfaker

Loren G. Terveen

Haiyi Zhu

Papers citing "Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems"

8 / 8 papers shown

Title
Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system Sumit Asthana Sagi Hilleli Pengcheng He Aaron L Halfaker 65 11 0 28 Jul 2023
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support Anna Kawakami Venkatesh Sivaraman H. Cheng Logan Stapleton Yanghuidi Cheng Diana Qing Adam Perer Zhiwei Steven Wu Haiyi Zhu Kenneth Holstein 51 109 0 05 Apr 2022
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova Aaron Roth FaML 89 413 0 20 Oct 2018
A Reductions Approach to Fair Classification Alekh Agarwal A. Beygelzimer Miroslav Dudík John Langford Hanna M. Wallach FaML 74 1,094 0 06 Mar 2018
A comparative study of fairness-enhancing interventions in machine learning Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian Sonam Choudhary Evan P. Hamilton Derek Roth FaML 73 639 0 13 Feb 2018
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 200 4,229 0 22 Jun 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 95 2,854 0 14 Mar 2017
Surviving an "Eternal September" - How an Online Community Managed a Surge of Newcomers C. Kiene Andrés Monroy-Hernández Benjamin Mako Hill 18 94 0 28 May 2016