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"

12 / 12 papers shown

Title
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
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 35 11 0 28 Jul 2023
`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values R. Varanasi Nitesh Goyal 26 46 0 14 Jul 2023
"Thoughts & Prayers'' or ":Heart Reaction: & :Prayer Reaction:'': How the Release of New Reactions on CaringBridge Reshapes Supportive Communication During Health Crises C. E. Smith Hannah Miller Hillberg Zachary Levonian 11 1 0 14 Apr 2023
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders Logan Stapleton Min Hun Lee Diana Qing Mary-Frances Wright Alexandra Chouldechova Kenneth Holstein Zhiwei Steven Wu Haiyi Zhu 38 55 0 18 May 2022
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation Vivian Lai Samuel Carton Rajat Bhatnagar Vera Liao Yunfeng Zhang Chenhao Tan 18 129 0 25 Apr 2022
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 28 106 0 05 Apr 2022
Jury Learning: Integrating Dissenting Voices into Machine Learning Models Mitchell L. Gordon Michelle S. Lam J. Park Kayur Patel Jeffrey T. Hancock Tatsunori Hashimoto Michael S. Bernstein 19 146 0 07 Feb 2022
Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" Fernando Delgado Stephen Yang Michael A. Madaio Qian Yang 20 55 0 01 Nov 2021
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance Gagan Bansal Tongshuang Wu Joyce Zhou Raymond Fok Besmira Nushi Ece Kamar Marco Tulio Ribeiro Daniel S. Weld 23 577 0 26 Jun 2020
Disseminating Research News in HCI: Perceived Hazards, How-To's, and Opportunities for Innovation C. E. Smith Eduardo Nevarez Haiyi Zhu 16 19 0 14 Jan 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 242 3,681 0 28 Feb 2017