497 citations · 1.9k across the 35 of their papers we have counts for
21 papers · 1 filter
Story Arena: A Multi-Agent Environment for Envisioning the Future of Software Engineering
Justin D. Weisz, Michael Muller, Kush R. Varshney
What better way to understand the impact of AI on software engineering than to ask AI itself? We constructed Story Arena, a multi-agent "writer's room" in which multiple AI agents,…
Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
Hyo Jin Do, Rachel Ostrand, Werner Geyer +3
Large language models (LLMs) are susceptible to generating inaccurate or false information, often referred to as "hallucinations" or "confabulations." While several technical advan…
An LLM's Attempts to Adapt to Diverse Software Engineers' Problem-Solving Styles: More Inclusive & Equitable?
Andrew Anderson, David Piorkowski, Margaret Burnett +1
Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. W…
Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation
Jessica He, Stephanie Houde, Justin D. Weisz
AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contrib…
Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach
Stephanie Houde, Kristina Brimijoin, Michael Muller +6
Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, t…
Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise
Justin D. Weisz, Shraddha Kumar, Michael Muller +4
AI assistants are being created to help software engineers conduct a variety of coding-related tasks, such as writing, documenting, and testing code. We describe the use of the wat…
Expedient Assistance and Consequential Misunderstanding: Envisioning an Operationalized Mutual Theory of Mind
Justin D. Weisz, Michael Muller, Arielle Goldberg +1
Design fictions allow us to prototype the future. They enable us to interrogate emerging or non-existent technologies and examine their implications. We present three design fictio…
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions
Hyo Jin Do, Rachel Ostrand, Justin D. Weisz +5
While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advanc…
Design Principles for Generative AI Applications
Justin D. Weisz, Jessica He, Michael Muller +3
Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need…
The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development
Steven I. Ross, Fernando Martinez, Stephanie Houde +2
Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natura…
Toward General Design Principles for Generative AI Applications
Justin D. Weisz, Michael Muller, Jessica He +1
Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on h…
Better Together? An Evaluation of AI-Supported Code Translation
Justin D. Weisz, Michael Muller, Steven I. Ross +5
Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from co…
Investigating Explainability of Generative AI for Code through Scenario-based Design
Jiao Sun, Q. Vera Liao, Michael Muller +4
What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative mo…
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Shweta Narkar, Yunfeng Zhang, Q. Vera Liao +2
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.…
Perfection Not Required? Human-AI Partnerships in Code Translation
Justin D. Weisz, Michael Muller, Stephanie Houde +5
Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised ne…
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
April Yi Wang, Dakuo Wang, Jaimie Drozdal +6
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code,…
AutoDS: Towards Human-Centered Automation of Data Science
Dakuo Wang, Josh Andres, Justin Weisz +2
Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data scientists and domain experts (e.g., data exploration, model training, etc.). Only til…
Expanding Explainability: Towards Social Transparency in AI systems
Upol Ehsan, Q. Vera Liao, Michael Muller +2
As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in h…
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Dakuo Wang, Justin D. Weisz, Michael Muller +6
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, k…
Designing for Democratization: Introducing Novices to Artificial Intelligence Via Maker Kits
Victor Dibia, Aaron Cox, Justin Weisz
Existing research highlight the myriad of benefits realized when technology is sufficiently democratized and made accessible to non-technical or novice users. However, democratizin…
CardKit: A Card-Based Programming Framework for Drones
Saad Ismail, Justin G. Manweiler, Justin D. Weisz
Drones are being used in many industries for a variety of applications, including inspecting bridges, surveying farm land, and delivering cargo. Automating these kinds of scenarios…