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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Building a Visual Analytics Tool for Understanding Machine Learning Models in Non-technical Domains |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Artificial intelligence (AI) is becoming increasingly important as the amount of digital data grows. However, AI systems are often opaque and perceived as black boxes, which has a negative impact on user acceptance and trust. We see this in healthcare, where despite the great potential of AI, a lack of understanding and trust has held back physicians from adopting it. One way to address these issues is through Explainable AI (XAI), which focuses on understanding and interpreting AI behavior. In this thesis, we want to contribute to XAI by developing a visual analytics system called VisAIExplorer. We want to find out how an interactive visual analytics system can be designed to explain machine learning models to novice machine learning users, and what types of visualizations within the system can help to build understanding. The goal of VisAIExplorer is to explain the two models, logistic regression and hierarchical clustering, to novice machine learning users by providing various visualizations and support throughout the work process. The machine learning models are trained on a medical dataset about strokes, as healthcare professionals could benefit from a better understanding and increased trust in AI systems. By improving transparency and user support, VisAIExplorer aims to overcome the limitations of existing AI tools and promote more explainability in AI. The thesis includes a literature review, system development, evaluation, and suggestions for future improvements. |
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