44 citations · 104 across the 47 of their papers we have counts for
11 papers · 1 filter
Humans are Missing from AI Coding Agent Research
Zora Z. Wang, John Yang, Kilian Lieret +10
Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebase…
Developer Interaction Patterns with Proactive AI: A Five-Day Field Study
Nadine Kuo, Agnia Sergeyuk, Valerie Chen +1
Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without expl…
Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
Rebecca Yu, Valerie Chen, Ameet Talwalkar +1
Growing excitement around deploying AI across various domains calls for a careful assessment of how human decision-makers interact with AI-powered systems. In particular, it is ess…
CodingGenie: A Proactive LLM-Powered Programming Assistant
Sebastian Zhao, Alan Zhu, Hussein Mozannar +3
While developers increasingly adopt tools powered by large language models (LLMs) in day-to-day workflows, these tools still require explicit user invocation. To seamlessly integra…
When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback
Jane Pan, Ryan Shar, Jacob Pfau +3
Programming is a fundamentally interactive process, yet coding assistants are often evaluated using static benchmarks that fail to measure how well models collaborate with users. W…
Need Help? Designing Proactive AI Assistants for Programming
Valerie Chen, Alan Zhu, Sebastian Zhao +3
While current chat-based AI assistants primarily operate reactively, responding only when prompted by users, there is significant potential for these systems to proactively assist…
Automation from the Worker's Perspective
Ben Armstrong, Valerie K. Chen, Alex Cuellar +2
Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern…
Modulating Language Model Experiences through Frictions
Katherine M. Collins, Valerie Chen, Ilia Sucholutsky +6
Language models are transforming the ways that their users engage with the world. Despite impressive capabilities, over-consumption of language model outputs risks propagating unch…
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
Matthew Barker, Emma Kallina, Dhananjay Ashok +6
Even though machine learning (ML) pipelines affect an increasing array of stakeholders, there is little work on how input from stakeholders is recorded and incorporated. We propose…
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan +1
AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on th…
Use-Case-Grounded Simulations for Explanation Evaluation
Valerie Chen, Nari Johnson, Nicholay Topin +2
A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use…