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198198 https://hdl.handle.net/2286/R.2.N.198198 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2019 231 pages Doctoral Dissertation Academic theses Text eng Huang, Jinbin Bryan, Chris Maciejewski, Ross Seifi, Hasti Kwon, Bum Chul Arizona State University Partial requirement for: Ph.D., Arizona State University, 2019 Field of study: Computer Science Artificial Intelligence (AI) technology has advanced significantly, enabling AI models to learn from diverse data and automate tasks previously performed solely by humans. This capability has excited people from various domains outside the AI research community and has driven technical experts from non-AI backgrounds to leverage AI for their domain-specific tasks. However, when these experts attempt to use AI, they face several challenges: understanding AI models' behavior and results in intuitive ways, adapting pre-trained models to their own datasets, and finding comprehensive guidelines for AI integration practices. This dissertation takes a user-centered approach to address these challenges by designing and developing interactive systems and frameworks that make AI more interpretable and accessible. The dissertation focuses on three key areas: Explaining AI Behavior: For domain experts from a non-AI background, understanding AI models is challenging. Automated explanations alone often fall short, as users need an iterative approach to form, test, and refine hypotheses. We introduce two visual analytics systems, ConceptExplainer and ASAP, designed to provide intuitive explanations and analysis tools, helping users better comprehend and interpret AI's inner workings and outcomes. Simplifying AI Workflows: Adapting pre-trained AI models for specific downstream tasks can be challenging for users with limited AI expertise. We present InFiConD, an interactive no-code interface for streamlining the knowledge distillation process, allowing users to easily adapt large models to their specific needs. Integrating AI in Domain-Specific Tasks: The integration of AI into domain-specific visual analytics systems is growing, but clear guidance is often lacking. Users with non-AI backgrounds face challenges in selecting appropriate AI tools and avoiding common pitfalls due to the vast array of available options. Our survey, AI4DomainVA, addresses this gap by reviewing existing practices and developing a roadmap for AI integration. This guide helps domain experts understand the synergy between AI and visual analytics, choose suitable AI methods, and effectively incorporate them into their workflows. Computer Science Data Visualization Human Computer Interaction Machine learning Explain, Simplify, and Integrate Artificial Intelligence with Visual Analyitics