Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/131362
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Type: Conference paper
Title: Human-AI interactive and continuous sensemaking: A case study of image classification using scribble attention maps
Author: Shen, H.
Liao, K.
Liao, Z.
Doornberg, J.
Qiao, M.
Van Den Hengel, A.
Verjans, J.W.
Citation: Proceedings of the Conference on Human Factors in Computing Systems (CHI'21), 2021 / Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T. (ed./s), pp.1-8
Publisher: Association for Computing Machinery
Publisher Place: New York, NY
Issue Date: 2021
ISBN: 9781450380959
Conference Name: International Conference on Human Factors in Computing Systems (CHI) (8 May 2021 - 13 May 2021 : virtual online)
Editor: Kitamura, Y.
Quigley, A.
Isbister, K.
Igarashi, T.
Statement of
Responsibility: 
Haifeng Shen, Kewen Liao, Zhibin Liao, Job Doornberg, Maoying Qiao, Anton van den Hengel, Johan W. Verjans
Abstract: Advances in Artificial Intelligence (AI), especially the stunning achievements of Deep Learning (DL) in recent years, have shown AI/DL models possess remarkable understanding towards the logic reasoning behind the solved tasks. However, human understanding towards what knowledge is captured by deep neural networks is still elementary and this has a detrimental effect on human’s trust in the decisions made by AI systems. Explainable AI (XAI) is a hot topic in both AI and HCI communities in order to open up the blackbox to elucidate the reasoning processes of AI algorithms in such a way that makes sense to humans. However, XAI is only half of human-AI interaction and research on the other half - human’s feedback on AI explanations together with AI making sense of the feedback - is generally lacking. Human cognition is also a blackbox to AI and effective human-AI interaction requires unveiling both blackboxes to each other for mutual sensemaking. The main contribution of this paper is a conceptual framework for supporting effective human-AI interaction, referred to as interactive and continuous sensemaking (HAICS). We further implement this framework in an image classification application using deep Convolutional Neural Network (CNN) classifiers as a browser-based tool that displays network attention maps to the human for explainability and collects human’s feedback in the form of scribble annotations overlaid onto the maps. Experimental results using a real-world dataset has shown significant improvement of classification accuracy (the AI performance) with the HAICS framework.
Keywords: interactive sensemaking; explainable AI; image classifcation; attention map; scribble interaction
Rights: © 2021 Association for Computing Machinery.
DOI: 10.1145/3411763.3451798
Published version: https://dl.acm.org/doi/book/10.1145/3411763
Appears in Collections:Aurora harvest 8
Australian Institute for Machine Learning publications

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