Publikation: ModelSpeX : Model Specification Using Explainable Artificial Intelligence Methods
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2020
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Deutsche Forschungsgemeinschaft (DFG): 422037984
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European Union (EU): 830892
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ARCHAMBAULT, Daniel, Hrsg. und andere. Machine Learning Methods in Visualisation for Big Data 2020. Genf: The Eurographics Association, 2020, S. 7-11. ISBN 978-3-03868-113-7. Verfügbar unter: doi: 10.2312/mlvis.20201100
Zusammenfassung
Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract human-centered rule-sets to generate model specifications from black-box models (e.g., neural networks). The workflow enables to reason about the underlying problem, to extract decision rule sets, and to evaluate the suitability of the model for a particular task. An exemplary usage scenario walks an analyst trough the steps of the workflow to show the applicability.
Zusammenfassung in einer weiteren Sprache
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004 Informatik
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International Workshop on Machine Learning in Visualisation for Big Data : MLVis 2020, 25. Mai 2020 - 29. Mai 2020, Norrköping, Sweden
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SCHLEGEL, Udo, Eren CAKMAK, Daniel A. KEIM, 2020. ModelSpeX : Model Specification Using Explainable Artificial Intelligence Methods. International Workshop on Machine Learning in Visualisation for Big Data : MLVis 2020. Norrköping, Sweden, 25. Mai 2020 - 29. Mai 2020. In: ARCHAMBAULT, Daniel, Hrsg. und andere. Machine Learning Methods in Visualisation for Big Data 2020. Genf: The Eurographics Association, 2020, S. 7-11. ISBN 978-3-03868-113-7. Verfügbar unter: doi: 10.2312/mlvis.20201100BibTex
@inproceedings{Schlegel2020Model-49721,
year={2020},
doi={10.2312/mlvis.20201100},
title={ModelSpeX : Model Specification Using Explainable Artificial Intelligence Methods},
isbn={978-3-03868-113-7},
publisher={The Eurographics Association},
address={Genf},
booktitle={Machine Learning Methods in Visualisation for Big Data 2020},
pages={7--11},
editor={Archambault, Daniel},
author={Schlegel, Udo and Cakmak, Eren and Keim, Daniel A.}
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