83 citations · 271 across the 8 of their papers we have counts for
6 papers · 1 filter
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Nari Johnson, Ángel Alexander Cabrera, Gregory Plumb +1
Machine learning (ML) models that achieve high average accuracy can still underperform on semantically coherent subsets ("slices") of data. This behavior can have significant socie…
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…
Symphony: Composing Interactive Interfaces for Machine Learning
Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman +5
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, r…
Discovering and Validating AI Errors With Crowdsourced Failure Reports
Ángel Alexander Cabrera, Abraham J. Druck, Jason I. Hong +1
AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant dev…