Ramsey, A., Kale, A., Kassa, Y., Gandhi, R., & Ricks, B. (2023). Toward Interactive Visualizations for Explaining Machine Learning Models. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 837–852). Omaha, USA: University of Nebraska at Omaha.
Abstract: Researchers and end users generally demand more trust and transparency from Machine learning (ML) models due to the complexity of their learned rule spaces. The field of eXplainable Artificial Intelligence (XAI) seeks to rectify this problem by developing methods of explaining ML models and the attributes used in making inferences. In the area of structural health monitoring of bridges, machine learning can offer insight into the relation between a bridge’s conditions and its environment over time. In this paper, we describe three visualization techniques that explain decision tree (DT) ML models that identify which features of a bridge make it more likely to receive repairs. Each of these visualizations enable interpretation, exploration, and clarification of complex DT models. We outline the development of these visualizations, along with their validity by experts in AI and in bridge design and engineering. This work has inherent benefits in the field of XAI as a direction for future research and as a tool for interactive visual explanation of ML models.
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Robin Gandhi, Deepak Khazanchi, Daniel Linzell, Brian Ricks, & Chungwook Sim. (2018). The Hidden Crisis : Developing Smart Big Data pipelines to address Grand Challenges of Bridge Infrastructure health in the United States. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1016–1021). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: The American Society of Civil Engineers (ASCE) Report Card for America's Infrastructure gave bridges a C+ (mediocre) grade in 2017. Approximately, 1 in 5 rural bridges are in critical condition, which presents serious challenges to public safety and economic growth. Fortunately, during a series of workshops on this topic organized by the authors, it has become clear that Big Data could provide a timely solution to these critical problems. In this work in progress paper we describe a conceptual framework for developing SMart big data pipelines for Aging Rural bridge Transportation Infrastructure (SMARTI). Our framework and associated research questions are organized around four ingredients: o Next-Generation Health Monitoring: Sensors; Unmanned Aerial Vehicle/System (UAV/UAS); wireless networks o Data Management: Data security and quality; intellectual property; standards and shared best practices; curation o Decision Support Systems: Analysis and modeling; data analytics; decision making; visualization, o Socio-Technological Impact: Policy; societal, economic and environmental impact; disaster and crisis management.
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