Discovering and Validating AI Errors With Crowdsourced Failure Reports

23 September 2021

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

6 / 6 papers shown

Title
Improving Human-AI Collaboration With Descriptions of AI Behavior Ángel Alexander Cabrera Adam Perer Jason I. Hong 12 33 0 06 Jan 2023
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 8 57 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 11 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 6 120 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 192 730 0 13 Dec 2018