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

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.

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 5 pages

References in corpus (4)

• Expanding Explainability: Towards Social Transparency in AI systems
• Interactive Model Cards: A Human-Centered Approach to Model Documentation
• Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation
• Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes