Zhiping(Arya) Zhang

Hi, I'm Zhiping(Arya) Zhang

/ʒiː pɪŋ ʒɑŋ/

👂

arya.zhangpika@gmail.com

I'm a researcher,

designer,

maker,

and mum of 6 kitties.

🐈🐈🐈🐈‍⬛🐈‍⬛🐈‍⬛

🎉

Starting Fall 2024, I'll be a PhD student at Northeastern University in the Khoury College of Computer Sciences supervised by Prof. Tianshi Li.

My research interests lie in Human-Robot (AI) Interaction, Responsible Technology, Behavior Change, and Embodied Interaction. My primary goals are to:

(1) understand how people perceive and interact with robots or agents,

(2) and use this insight to design robots that positively impact people’s behavior or cognition and truly benefit to people in a responsible and respectful way.

To support these goals, I used mixed methods and research through design, building early-stage prototypes and leveraging design and technology as research tools.

You can refer to my CHI’24 papers advised by Prof. Tianshi Li and Prof. Dakuo Wang (NEU Human-Centered AI Lab); and my coauthored HRI’20 paper advised by Prof . Emilia Barakova and Prof. Panos Markopoulos (TU/e Social Robotics Lab).

With my industry background in Human-AI interaction systems, including roles as a UX researcher and designer at ALIBABA, an AI product manager and creative technologist at FITURE, I value collaboration in multidisciplinary teams. This ensure that the systems we develop are genuinely integrated into users’ lives and truly make a positive impact.

My landed projects brought tangible benefits to users and awarded by top international events.

(2024 IF Design Award, 2023 Reddot Award, 2020 IF Talent Award, 2018 Dutch Design Week)

I do research.

I approach complex questions using human-centered design methods, including mixed methods and research through design. My primary goal is to understand how people interact with robots or agents, like their perceptions or mental models, and to create agents that truly benefit people. I'm interested in guiding behavior changes to enhance health and wellbeing or foster responsible technology use.

CHI

“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 (Hank) Lee, Bingsheng Yao, Sauvik Das, Ada Lerner, Dakuo Wang, and Tianshi Li

In CHI Conference on Human Factors in Computing Systems Apr 2024

. 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 that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users’ perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users’ erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users’ ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigmatic shifts to protect the privacy of LLM-based CA users.

CHI SIG

Human-Centered Privacy Research in the Age of Large Language Models

🙌 Welcome to our incoming Special Interest Group in CHI 2024 to discuss more ❯

🗺️ Room 318A | 📅 May 13th (Mon)

Tianshi Li, Sauvik Das, Hao-Ping (Hank) Lee, Dakuo Wang, Bingsheng Yao and Zhiping Zhang
In CHI Conference on Human Factors in Computing Systems (CHI’24 Companion) Apr 2024

.

. 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 has been model-centered: exploring how LLMs lead to privacy risks like memorization, or can be used to infer personal characteristics about people from their content. We argue that there is a need for more research focusing on the human aspect of these privacy issues: e.g., research on how design paradigms for LLMs affect users' disclosure behaviors, users' mental models and preferences for privacy controls, and the design of tools, systems, and artifacts that empower end-users to reclaim ownership over their personal data. To build usable, efficient, and privacy-friendly systems powered by these models with imperfect privacy properties, our goal is to initiate discussions to outline an agenda for conducting human-centered research on privacy issues in LLM-powered systems. This Special Interest Group (SIG) aims to bring together researchers with backgrounds in usable security and privacy, human-AI collaboration, NLP, or any other related domains to share their perspectives and experiences on this problem, to help our community establish a collective understanding of the challenges, research opportunities, research methods, and strategies to collaborate with researchers outside of HCI.

PDF

social robot for musical instrument practice

HRI

Robot Role Design for Implementing Social Facilitation Theory in Musical Instruments Practicing

Heqiu Song, Zhiping Zhang, Emilia I. Barakova, Jaap Ham and Panos Markopoulos
In HRI Conference on Human-Robot Interaction 2020

.

. The application of social robots has recently been explored in various types of educational settings including music learning. Earlier research presented evidence that simply the presence of a robot can influence a person’s task performance, confirming social facilitation theory and findings in human-robot interaction. Confirming the evaluation apprehension theory, earlier studies showed that next to a person’s presence, also that person’s social role could influence a user’s performance: the presence of a (non-) evaluative other can influence the user’s motivation and performance differently. To be able to investigate that, researchers need the roles for the robot which is missing now. In the current research, we describe the design of two social roles (i.e., evaluative role and non-evaluative role) of a robot that can have different appearances. For this, we used the SocibotMini: A robot with a projected face, allowing diversity and great flexibility of human-like social cue presentation. An empirical study at a real practice room including 20 participants confirmed that users (i.e., children) evaluated the robot roles as intended. Thereby, the current research provided the robot roles allowing to study whether the presence of social robots in certain social roles can stimulate practicing behavior and suggestions of how such roles can be designed and improved. Future studies can investigate how the presence of a social robot in a certain social role can stimulate children to practice.

PDF

Understanding Secret Use of Large Language Models

Zhiping Zhang, Chenxinran Shen, Bingsheng Yao, Dakuo Wang, and Tianshi Li

Under Review, 2024

I design and realize.

I believe that good design truly shines when it integrates seamlessly into users' lives, bringing tangible benefits. I enjoy bridging theory with practical needs to create applications that matter. Here are my selected landed projects that had great impact on our users, achieved commercial success, and earned top international awards. I often incorporated the concept of embodied interaction in my designs, enabling technology such as AI to benefit users.