Search Results for author: Anamaria Crisan

Found 6 papers, 0 papers with code

Conversational AI Threads for Visualizing Multidimensional Datasets

no code implementations9 Nov 2023 Matt-Heun Hong, Anamaria Crisan

From these findings, we developed AI Threads, a multi-threaded analytic chatbot that enables analysts to proactively manage conversational context and improve the efficacy of its outputs.

Chatbot

Eliciting Model Steering Interactions from Users via Data and Visual Design Probes

no code implementations12 Oct 2023 Anamaria Crisan, Maddie Shang, Eric Brochu

In this research, we conduct an elicitation study using data and visual design probes to examine if and how experts with a spectrum of ML expertise use semantic interactions to update a simple classification model.

Interactive Model Cards: A Human-Centered Approach to Model Documentation

no code implementations5 May 2022 Anamaria Crisan, Margaret Drouhard, Jesse Vig, Nazneen Rajani

Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML).

Ethics

Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop

no code implementations12 Jan 2021 Anamaria Crisan, Brittany Fiore-Gartland

AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science.

AutoML Data Visualization

User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics

no code implementations6 Jan 2021 Anamaria Crisan, Michael Correll

Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora.

Clustering Topic Models

On Regulatory and Organizational Constraints in Visualization Design and Evaluation

no code implementations31 Oct 2016 Anamaria Crisan, Jennifer L. Gardy, Tamara Munzner

This lack of more explicit guidance can leave visualization researchers and practitioners vulnerable to unforeseen constraints beyond the user's needs that can affect the validity of evaluations, or even lead to the premature termination of a project.

Human-Computer Interaction

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