61 citations · 113 across the 4 of their papers we have counts for
6 papers
"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…
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Gregory Plumb, Maruan Al-Shedivat, Eric Xing +1
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post…
Regularizing Black-box Models for Improved Interpretability
Gregory Plumb, Maruan Al-Shedivat, Angel Alexander Cabrera +3
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post…
Interactive Classification for Deep Learning Interpretation
Ángel Alexander Cabrera, Fred Hohman, Jason Lin +1
We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers. Using modern web technologies to…