Search Results for author:
Found 14 papers, 10 papers with code
DiffusionWorldViewer: Exposing and Broadening the Worldview Reflected by Generative Text-to-Image Models
Generative text-to-image (TTI) models produce high-quality images from short textual descriptions and are widely used in academic and creative domains.
VisText: A Benchmark for Semantically Rich Chart Captioning
Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities.
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.
Teaching Humans When To Defer to a Classifier via Exemplars
For this collaboration to perform properly, the human decision maker must have a mental model of when and when not to rely on the agent.
LMdiff: A Visual Diff Tool to Compare Language Models
While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other.
Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content
To demonstrate how our model can be applied to evaluate the effectiveness of visualization descriptions, we conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful.
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior.
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most.
Decision Making
Human-Computer Interaction
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models.
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them.
Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics.
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery.
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs.
The Building Blocks of Interpretability
In this article, we treat existing interpretability methods as fundamental and composable building blocks for rich user interfaces.
