Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects

9 February 2020

Papers citing "Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects"

11 / 11 papers shown

Title
From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap Tianqi Kou 36 0 0 19 Apr 2024
Generative AI in the Wild: Prospects, Challenges, and Strategies Yuan Sun Eunchae Jang Fenglong Ma Ting Wang 32 21 0 03 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
MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities Katherine R. Maffey Kyle Dotterrer Jennifer Niemann Iain J. Cruickshank Grace A. Lewis Christian Kastner 22 4 0 03 Mar 2023
AutoML in The Wild: Obstacles, Workarounds, and Expectations Yuan Sun Qiurong Song Xinning Gui Fenglong Ma Ting Wang 16 13 0 21 Feb 2023
An Empirical Study on the Usage of Automated Machine Learning Tools Forough Majidi Moses Openja Foutse Khomh Heng Li 42 14 0 28 Aug 2022
The Fallacy of AI Functionality Inioluwa Deborah Raji Indra Elizabeth Kumar Aaron Horowitz Andrew D. Selbst 25 179 0 20 Jun 2022
Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science S. Cambo Darren Gergle 17 34 0 08 Mar 2022
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 27 85 0 28 Jul 2021
Data Vision: Learning to See Through Algorithmic Abstraction Samir Passi S. Jackson 135 111 0 09 Feb 2020
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems Jaimie Drozdal Justin D. Weisz Dakuo Wang Gaurav Dass Bingsheng Yao Changruo Zhao Michael J. Muller Lin Ju Hui Su 36 124 0 17 Jan 2020