Browsing by Author "Hyvärinen, Linda"

With the increased usage of machine learning models in various tasks and domains, the demand of understanding the models is emphasized. However, often modern machine learning models are difficult to understand and therefore do not provoke trust. Models can be understood by revealing their inner logic with explanations, but explanations can be difficult to interpret for non-expert users. We introduce an interactive visual interface to help non-expert users to understand and compare machine learning models. The interface visualizes explanations for multiple models in order to help the user to understand how the models generate predictions and whether the predictions can be trusted. We also explore current research in explainable AI visualizations, in order to compare our prototype to comparable systems present in research. The contributions of this paper are a system description and a use case for an interactive visualization interface to compare and explain machine learning models, as well as providing an understanding of the current state of research in explainable AI visualization systems and recommendations for future studies. We conclude that our system enables efficient visualizations for regression models unlike the papers covered in our survey. Another conclusion is that the field lacks precise terminology.