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20202026
most citedExploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

96 citations · 234 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.HC2023

Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice

Wesley Hanwen Deng, Nur Yildirim, Monica Chang +3

An emerging body of research indicates that ineffective cross-functional collaboration -- the interdisciplinary work done by industry practitioners across roles -- represents a maj…

cs.HC2023★ 2 cited

Towards "Anytime, Anywhere" Community Learning and Engagement around the Design of Public Sector AI

Wesley Hanwen Deng, Motahhare Eslami, Kenneth Holstein

Data-driven algorithmic and AI systems are increasingly being deployed to automate or augment decision processes across a wide range of public service settings. Yet community membe…

cs.HC2022★ 72 cited

Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice

Wesley Hanwen Deng, Bill Boyuan Guo, Alicia DeVrio +3

Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problema…

cs.HC2022★ 1 cited

Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust

Wesley Hanwen Deng, Nikita Mehandru, Samantha Robertson +1

Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT…

cs.HC2022★ 96 cited

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee +4

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has…