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[Track and Know] Big Data for Mobility Tracking Knowledge Extraction in Urban Areas (780754)
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  • Publication . Part of book or chapter of book . Conference object . Preprint . Article . 2020
    Open Access English
    Authors: 
    Riccardo Guidotti; Anna Monreale; Stan Matwin; Dino Pedreschi;
    Country: Italy
    Project: EC | PRO-RES (788352), EC | SoBigData (654024), EC | AI4EU (825619), EC | Track and Know (780754), NSERC

    We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by "morphing" into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.

  • Open Access English
    Authors: 
    Christopher Collins; Natalia Andrienko; Tobias Schreck; Jing Yang; Jaegul Choo; Ulrich Engelke; Amit Jena; Tim Dwyer;
    Publisher: Elsevier
    Country: United Kingdom
    Project: EC | Track and Know (780754)

    In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making. Keywords: Guidance, Visual analytics, Model evaluation

Advanced search in
Research products
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[Track and Know] Big Data for Mobility Tracking Knowledge Extraction in Urban Areas (780754)
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
2 Research products, page 1 of 1
  • Publication . Part of book or chapter of book . Conference object . Preprint . Article . 2020
    Open Access English
    Authors: 
    Riccardo Guidotti; Anna Monreale; Stan Matwin; Dino Pedreschi;
    Country: Italy
    Project: EC | PRO-RES (788352), EC | SoBigData (654024), EC | AI4EU (825619), EC | Track and Know (780754), NSERC

    We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by "morphing" into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.

  • Open Access English
    Authors: 
    Christopher Collins; Natalia Andrienko; Tobias Schreck; Jing Yang; Jaegul Choo; Ulrich Engelke; Amit Jena; Tim Dwyer;
    Publisher: Elsevier
    Country: United Kingdom
    Project: EC | Track and Know (780754)

    In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making. Keywords: Guidance, Visual analytics, Model evaluation