Evaluating Human-in-the-Loop Approaches to Decision Support in Interactive Data Science Platforms
Keywords:
Human-in-the-Loop, Interactive Data Science, Decision Support Systems, Machine Learning, User InteractionSynopsis
Human-in-the-Loop (HITL) approaches embed human judgment within computational decision support frameworks to improve accuracy, interpretability, and trust in interactive data science platforms. This study investigates the design principles, challenges, and empirical performance of HITL methods for decision support, emphasizing interactive analytics workflows. We present mock experimental data comparing automated, semi-automated (HITL), and fully manual workflows, discuss implications for system design, and identify research gaps. Findings suggest HITL integration enhances decision quality while balancing cognitive workload and performance.
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