87 citations · 91 across the 7 of their papers we have counts for
6 papers · 1 filter
When Are LLM Inferences Acceptable? User Reactions and Control Preferences for Inferred Personal Information
Kyzyl Monteiro, Minjung Park, Alexander Ioffrida +6
Ask ChatGPT about vacation planning, and it may infer your income. Ask it about medication, and it may infer your medical history. Because such inferences can expose more informati…
Promoting Critical Thinking With Domain-Specific Generative AI Provocations
Thomas Şerban von Davier, Hao-Ping Lee, Jodi Forlizzi +1
The evidence on the effects of generative AI (GenAI) on critical thinking is mixed, with studies suggesting both potential harms and benefits depending on its implementation. Some…
Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts
Hao-Ping Lee, Yu-Ju Yang, Matthew Bilik +7
AI creates and exacerbates privacy risks, yet practitioners lack effective resources to identify and mitigate these risks. We present Privy, a tool that guides practitioners withou…
Human-Centered Privacy Research in the Age of Large Language Models
Tianshi Li, Sauvik Das, Hao-Ping Lee +3
The emergence of large language models (LLMs), and their increased use in user-facing systems, has led to substantial privacy concerns. To date, research on these privacy concerns…
Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks
Hao-Ping Lee, Yu-Ju Yang, Thomas Serban von Davier +2
Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy o…
"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
Zhiping Zhang, Michelle Jia, Hao-Ping Lee +5
The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs…