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Found 9 papers, 1 papers with code
Longitudinal Study on Social and Emotional Use of AI Conversational Agent
In this five-week, exploratory study, we recruited 149 participants divided into two usage groups: a baseline usage group (BU, n=60) that used the internet and AI as usual, and an active usage group (AU, n=89) encouraged to use one of four commercially available AI tools (Microsoft Copilot, Google Gemini, PI AI, ChatGPT) for social and emotional interactions.
From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents
To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through lived experience of individuals.
Data Analysis in the Era of Generative AI
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency
Writing is a well-established practice to support ideation and creativity.
From User Surveys to Telemetry-Driven Agents: Exploring the Potential of Personalized Productivity Solutions
We present a comprehensive, user-centric approach to understand preferences in AI-based productivity agents and develop personalized solutions tailored to users' needs.
Affective Conversational Agents: Understanding Expectations and Personal Influences
The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains.
Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes
Creating Computer Vision (CV) models remains a complex practice, despite their ubiquity.
NL-EDIT: Correcting semantic parse errors through natural language interaction
We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors.
Machine Teaching: A New Paradigm for Building Machine Learning Systems
This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them.
