27 citations · 80 across the 8 of their papers we have counts for
8 papers
MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models
Grace Guo, Lifu Deng, Animesh Tandon +2
The recent prevalence of publicly accessible, large medical imaging datasets has led to a proliferation of artificial intelligence (AI) models for cardiovascular image classificati…
What We Augment When We Augment Visualizations: A Design Elicitation Study of How We Visually Express Data Relationships
Grace Guo, John Stasko, Alex Endert
Visual augmentations are commonly added to charts and graphs in order to convey richer and more nuanced information about relationships in the data. However, many design spaces pro…
Visualizing Intelligent Tutor Interactions for Responsive Pedagogy
Grace Guo, Aishwarya Mudgal Sunil Kumar, Adit Gupta +3
Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstra…
Explainability in JupyterLab and Beyond: Interactive XAI Systems for Integrated and Collaborative Workflows
Grace Guo, Dustin Arendt, Alex Endert
Explainable AI (XAI) tools represent a turn to more human-centered and human-in-the-loop AI approaches that emphasize user needs and perspectives in machine learning model developm…
Causalvis: Visualizations for Causal Inference
Grace Guo, Ehud Karavani, Alex Endert +1
Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaboration…
VAINE: Visualization and AI for Natural Experiments
Grace Guo, Maria Glenski, ZhuanYi Shaw +4
Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as…
A Comparative Analysis of Industry Human-AI Interaction Guidelines
Austin P. Wright, Zijie J. Wang, Haekyu Park +6
With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. Howev…
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations
Fabian Sperrle, Mennatallah El-Assady, Grace Guo +3
This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches. These dimensions enable a descriptive cha…