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dc.contributor.authorBian, Yalien
dc.date.accessioned2022-03-07T22:48:58Zen
dc.date.available2022-03-07T22:48:58Zen
dc.date.issued2022-03-07en
dc.identifier.othervt_gsexam:33971en
dc.identifier.urihttp://hdl.handle.net/10919/109213en
dc.description.abstractHuman-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.en
dc.format.mediumETDen
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSemantic Interactionen
dc.subjectInteractive Deep Learningen
dc.subjectVisual Analyticsen
dc.subjectSensemakingen
dc.subjectExplainable AIen
dc.subjectHuman-in-the-loop Machine Learningen
dc.titleHuman-AI Sensemaking with Semantic Interaction and Deep Learningen
dc.typeDissertationen
dc.contributor.departmentComputer Scienceen
dc.description.degreeDoctor of Philosophyen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineComputer Science and Applicationsen
dc.contributor.committeechairNorth, Christopher L.en
dc.contributor.committeememberKarpatne, Anujen
dc.contributor.committeememberEldardiry, Hoda Mohameden
dc.contributor.committeememberReddy, Chandan K.en
dc.contributor.committeememberKrokos, Eric Peteren
dc.description.abstractgeneralHuman AI interaction can harness the separate strengths of human and machine intelligence to accomplish tasks neither can solve alone. Analysts are good at making high-level hypotheses and reasoning from their domain knowledge. AI models are better at data computation based on low-level input features. Successful human-AI interactions can perform real-world, high-stakes tasks, such as issuing medical diagnoses, making credit assessments, and determining cases of discrimination. Semantic interaction is a visual methodology providing intuitive communications between analysts and traditional machine learning models. It is commonly utilized to enhance visual analytics systems for sensemaking tasks, such as intelligence analysis and scientific research. The contribution of this work is to explore how to use semantic interaction to achieve collaboration between humans and state-of-the-art deep learning models for complex sensemaking tasks. To do this, I first evaluate the straightforward solution of integrating the pretrained deep learning model into the traditional semantic interaction pipeline. Results show that the deep learning representation matches human cognition better than hand engineering features via semantic interaction. Next, I look at methods for supporting semantic interaction systems with interactive and interpretable deep learning. The new pipeline provides effective communication between human and deep learning models. Interactive deep learning enables the system to better capture users' intents. Interpretable deep learning lets users have a clear understanding of models. Finally, I improve the pipeline to better support collaboration using a neural design. I hope this work can contribute to future designs for the human-in-the-loop analysis with deep learning and visual analytics techniques.en


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