Publications

REX: Designing User-centered Repair and Explanations to Address Robot Failures

Published in DIS 24: Proceedings of the 2024 ACM Conference on Designing Interactive Systems, 2024

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Abstract: Robots in real-world environments continuously engage with multiple users and encounter changes that lead to unexpected conflicts in fulfilling user requests. Recent technical advancements (e.g., large-language models (LLMs), program synthesis) offer various methods for automatically generating repair plans that address such conflicts. In this work, we understand how automated repair and explanations can be designed to improve user experience with robot failures through two user studies. In our first, online study ($n=162$), users expressed increased trust, satisfaction, and utility with the robot performing automated repair and explanations. However, we also identified risk factors—safety, privacy, and complexity—that require adaptive repair strategies. The second, in-person study ($n=24$) elucidated distinct repair and explanation strategies depending on the level of risk severity and type. Using a design-based approach, we explore automated repair with explanations as a solution for robots to handle conflicts and failures, complemented by adaptive strategies for risk factors. Finally, we discuss the implications of incorporating such strategies into robot designs to achieve seamless operation among changing user needs and environments.

Recommended citation: Christine P. Lee, Pragathi Praveena, and Bilge Mutlu. 2024. REX: Designing User-centered Repair and Explanations to Address Robot Failures. To appear in proceedings of the 2024 ACM Conference on Designing Interactive Systems (DIS 24).

The AI-DEC: A Card-based Design Method for User-centered AI Explanations

Published in DIS 24: Proceedings of the 2024 ACM Conference on Designing Interactive Systems, 2024

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Abstract: Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems—communication content, modality, frequency, and direction—and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system’s workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.

Recommended citation: Christine P. Lee, Min Kyung Lee*, and Bilge Mutlu*. 2024. The AI-DEC: A Card-based Design Method for User-centered AI Explanations. To appear in proceedings of the 2024 ACM Conference on Designing Interactive Systems (DIS 24).

Design, Development, and Deployment of Context-Adaptive AI Systems for Enhanced User Adoption

Published in CHI EA 24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, 2024

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Abstract: My research centers on the development of context-adaptive AI systems to improve end-user adoption through the integration of technical methods. I deploy these AI systems across various interaction modalities, including user interfaces and embodied agents like robots, to expand their practical applicability. My research unfolds in three key stages: design, development, and deployment. In the design phase, user-centered approaches were used to understand user experiences with AI systems and create design tools for user participation in crafting AI explanations. In the ongoing development stage, a safety-guaranteed AI system for a robot agent was created to automatically provide adaptive solutions and explanations for unforeseen scenarios. The next steps will involve the implementation and evaluation of context-adaptive AI systems in various interaction forms. I seek to prioritize human needs in technology development, creating AI systems that tangibly benefit end-users in real-world applications and enhance interaction experiences.

Recommended citation: Christine P. Lee. 2024. Design, Development, and Deployment of Context-Adaptive AI Systems for Enhanced User Adoption. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA 24). Association for Computing Machinery, New York, NY, USA, Article 429, 1–5.

Understanding Large-Language Model (LLM)-powered Human-Robot Interaction

Published in HRI 24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

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Abstract: While social robots are increasingly introduced into domestic settings, few have explored the utility of the robots’ packaging. Here we highlight the potential of product packaging in human-robot interaction to facilitate, expand, and enrich user experience with the robot. We present a social robot’s box as interactive product packaging, designed to be reused as a “home’’ for the robot. Through co-design sessions with children, an narrative-driven and socially engaging box was developed to support initial interactions between the child and the robot. Our findings emphasize the importance of packaging design to produce positive outcomes towards successful human-robot interaction.

Recommended citation: Callie Y. Kim*, Christine P. Lee*, and Bilge Mutlu. 2024. Understanding Large-Language Model (LLM)-powered Human-Robot Interaction. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI 24). Association for Computing Machinery, New York, NY, USA, 371–380.

Demonstrating the Potential of Interactive Product Packaging for Enriching Human-Robot Interaction

Published in HRI 23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 2023

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Abstract: While social robots are increasingly introduced into domestic settings, few have explored the utility of the robots’ packaging. Here we highlight the potential of product packaging in human-robot interaction to facilitate, expand, and enrich user experience with the robot. We present a social robot’s box as interactive product packaging, designed to be reused as a “home’’ for the robot. Through co-design sessions with children, an narrative-driven and socially engaging box was developed to support initial interactions between the child and the robot. Our findings emphasize the importance of packaging design to produce positive outcomes towards successful human-robot interaction.

Recommended citation: Christine P. Lee, Bengisu Cagiltay, Dakota Sullivan, and Bilge Mutlu. 2023. Demonstrating the Potential of Interactive Product Packaging for Enriching Human-Robot Interaction. In Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (HRI 23). Association for Computing Machinery, New York, NY, USA, 899–901.

The Unboxing Experience: Exploration and Design of Initial Interactions Between Children and Social Robots

Published in CHI 22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 2022

🎖️ Best Paper Honorable Mention

Recommended citation: Christine P Lee, Bengisu Cagiltay, and Bilge Mutlu. 2022. The Unboxing Experience: Exploration and Design of Initial Interactions Between Children and Social Robots. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI 22). Association for Computing Machinery, New York, NY, USA, Article 151, 1–14.