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Xinran Adeline Li

dblp:433/2712 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0000-4822-786XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 77% User interface design and tools · 23%
Network and information security
1 paper
Usable security · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › generative AI
generative AI chatbots
1.012026
Designing Privacy Choice in Generative AI Chatbot Ecosystems · CHI 2026
Usable security › usable privacy › privacy behavior
privacy choices
1.012026
Designing Privacy Choice in Generative AI Chatbot Ecosystems · CHI 2026
User interface design and tools › user interface design
privacy interfaces
0.312026
Designing Privacy Choice in Generative AI Chatbot Ecosystems · CHI 2026

Methods — techniques the papers use, named apart from their topics

vignette-based survey · 2.0interview study · 2.0
YearPublicationVenuePosition
2026 Designing Privacy Choice in Generative AI Chatbot Ecosystems
abstract
Generative AI (GenAI) is evolving from standalone tools to interconnected ecosystems that integrate chatbots, cloud platforms, and third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. Existing solutions focus on system-level protections, offering little support for users to make meaningful privacy choices. To address this gap, we conducted two vignette-based survey studies with 486 participants and a follow-up interview study with 16 participants. We also explored users’ needs and preferences for privacy choice design across both GenAI personalization and data-sharing. Our results reveal paradoxical patterns: participants sometimes trusted third-party ecosystems more for personalization but perceived greater control in first-party ecosystems when data was shared externally. We discuss design implications for privacy choice interfaces that enhance transparency, control, and trust in GenAI ecosystems.
Lanjing Liu, Xinran Adeline Li, Allen Yilun Lin, Yaxing Yao
CHI2