Human-AI Sensemaking with Semantic Interaction and Deep Learning


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Virginia Tech


Human-AI interaction can improve overall performance, exceeding the performance that either humans or AI could achieve separately, thus producing a whole greater than the sum of the parts. Visual analytics enables collaboration between humans and AI through interactive visual interfaces. Semantic interaction is a design methodology to enhance visual analytics systems for sensemaking tasks. It is widely applied for sensemaking in high-stakes domains such as intelligence analysis and academic research. However, existing semantic interaction systems support collaboration between humans and traditional machine learning models only; they do not apply state-of-the-art deep learning techniques.

The contribution of this work is the effective integration of deep neural networks into visual analytics systems with semantic interaction. More specifically, I explore how to redesign the semantic interaction pipeline to enable collaboration between human and deep learning models for sensemaking tasks. First, I validate that semantic interaction systems with pre-trained deep learning better support sensemaking than existing semantic interaction systems with traditional machine learning. Second, I integrate interactive deep learning into the semantic interaction pipeline to enhance inference ability in capturing analysts' precise intents, thereby promoting sensemaking. Third, I add semantic explanation into the pipeline to interpret the interactively steered deep learning model. With a clear understanding of DL, analysts can make better decisions. Finally, I present a neural design of the semantic interaction pipeline to further boost collaboration between humans and deep learning for sensemaking.



Semantic Interaction, Interactive Deep Learning, Visual Analytics, Sensemaking, Explainable AI, Human-in-the-loop Machine Learning