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David Ebert

Purdue University



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Jingjing Guo

Purdue University



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161 – Dynamic Interactive Data Visualization and Utilization

Visual Analytics Driven Human-Computer Collaborative Decision Making to Solve Global Challenges

Sponsor: Section on Statistical Graphics
Keywords: Human-Computer Collaborative Decision Making (HCCD), Decision Making Systems, Explainable AI (XAI), Artificial Intelligence, Visual Analytics, Interactive Machine Learning

David Ebert

Purdue University

Jingjing Guo

Purdue University

Growing accessibility and availability of data and cloud computing create new opportunities to rethink and redesign critical engineering systems and address outstanding global challenges. The machine learning approaches, having succeeded in many new fields of study, will have many issues as they get adopted in system of increasing complexity, scale and criticality. In this paper, we present the Human-Collaborative Decision Making (HCCD), a framework that leverages both artificial and human intelligences through visual analytics to produce explainable and trustable information that support decision making in complex and critical systems. We demonstrate the generalizability, superiority, impact and potential of HCCD in a wide range of applications.

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