Discovering and Validating AI Errors With Crowdsourced Failure Reports

Discovering and Validating AI Errors With Crowdsourced Failure Reports

23 September 2021

Ángel Alexander Cabrera

Abraham J. Druck

Papers citing "Discovering and Validating AI Errors With Crowdsourced Failure Reports"

9 / 9 papers shown

Title
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements Samantha Robertson Zijie J. Wang Dominik Moritz Mary Beth Kery Fred Hohman 32 15 0 12 Apr 2023
fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks Steven Moore Q. V. Liao Hariharan Subramonyam 19 27 0 22 Feb 2023
Improving Human-AI Collaboration With Descriptions of AI Behavior Ángel Alexander Cabrera Adam Perer Jason I. Hong 22 34 0 06 Jan 2023
Capabilities for Better ML Engineering Chenyang Yang Rachel A. Brower-Sinning Grace A. Lewis Christian Kastner Tongshuang Wu 24 3 0 11 Nov 2022
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
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers V. Pradhan M. Schaekermann Matthew Lease 23 12 0 04 Dec 2021
Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors Hong Shen Alicia DeVrio Motahhare Eslami Kenneth Holstein MLAU 16 122 0 06 May 2021
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