175 citations · 398 across the 17 of their papers we have counts for
5 papers · 1 filter
Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them
Allison Chen, Sunnie S. Y. Kim, Angel Franyutti +4
How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we ra…
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
Indu Panigrahi, Sunnie S. Y. Kim, Amna Liaqat +4
Explanations for computer vision models are important tools for interpreting how the underlying models work. However, they are often presented in static formats, which pose challen…
Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao +2
Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mit…
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Trust is an important factor in people's interactions with AI systems. However, there is a lack of empirical studies examining how real end-users trust or distrust the AI system th…
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations. To address this gap and…