83 citations · 245 across the 6 of their papers we have counts for
5 papers · 1 filter
Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?
Rena Li, Sara Kingsley, Chelsea Fan +6
Recent years have witnessed an interesting phenomenon in which users come together to interrogate potentially harmful algorithmic behaviors they encounter in their everyday lives.…
Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
Tzu-Sheng Kuo, Hong Shen, Jisoo Geum +4
Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their…
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci +4
Machine learning models with high accuracy on test data can still produce systematic failures, such as harmful biases and safety issues, when deployed in the real world. To detect…
Improving Human-AI Collaboration With Descriptions of AI Behavior
Ángel Alexander Cabrera, Adam Perer, Jason I. Hong
People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appr…
"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains
Hong Shen, Ángel Alexander Cabrera, Adam Perer +1
This position paper offers a framework to think about how to better involve human influence in algorithmic decision-making of contentious public policy issues. Drawing from insight…