14 citations · 41 across the 13 of their papers we have counts for
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Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships
Angie Boggust, Hyemin Bang, Hendrik Strobelt +1
While interpretability methods identify a model's learned concepts, they overlook the relationships between concepts that make up its abstractions and inform its ability to general…
DiffusionWorldViewer: Exposing and Broadening the Worldview Reflected by Generative Text-to-Image Models
Zoe De Simone, Angie Boggust, Arvind Satyanarayan +1
Generative text-to-image (TTI) models produce high-quality images from short textual descriptions and are widely used in academic and creative domains. Like humans, TTI models have…
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Angie Boggust, Harini Suresh, Hendrik Strobelt +2
Saliency methods are a common class of machine learning interpretability techniques that calculate how important each input feature is to a model's output. We find that, with the r…
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
Angie Boggust, Benjamin Hoover, Arvind Satyanarayan +1
Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior. However, interpreting…