activity
20232026
most cited"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents

87 citations · 91 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CY2026

The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products

Hao-Ping Lee, Jessica He, David Piorkowski +3

Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments. However, these same characteristics can create or exacerbate produ…

cs.HC2026

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…

cs.HC2026

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…

cs.CY2026

How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?

Yuxuan Li, Leyang Li, Hao-Ping Lee +1

A growing body of research assumes that large language model (LLM) agents can serve as proxies for how people form attitudes toward and behave in response to security and privacy (…

cs.HC2025

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…

cs.HC2024★ 2 cited

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…

cs.HC2023★ 2 cited

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…

cs.HC2023★ 87 cited

"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…