Mutual Explanations for Cooperative Decision Making in Medicine
Faculty/Professorship: | Cognitive Systems |
Author(s): | Schmid, Ute ![]() ![]() |
Title of the Journal: | Künstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen |
ISSN: | 1610-1987, 0933-1875 |
Publisher Information: | Berlin ; Heidelberg : Springer |
Year of publication: | 2020 |
Volume: | 34 |
Issue: | 2 |
Pages: | 227-233 |
Language(s): | English |
DOI: | 10.1007/s13218-020-00633-2 |
Abstract: | Exploiting mutual explanations for interactive learning is presented as part of an interdisciplinary research project on transparent machine learning for medical decision support. Focus of the project is to combine deep learning black box approaches with interpretable machine learning for classification of different types of medical images to combine the predictive accuracy of deep learning and the transparency and comprehensibility of interpretable models. Specifically, we present an extension of the Inductive Logic Programming system Aleph to allow for interactive learning. Medical experts can ask for verbal explanations. They can correct classification decisions and in addition can also correct the explanations. Thereby, expert knowledge can be taken into account in form of constraints for model adaption. |
GND Keywords: | Induktive logische Programmierung; Constraint <Künstliche Intelligenz>; Maschinelles Lernen; Entscheidungsunterstützung |
Keywords: | Human-AI partnership, Inductive Logic Programming, Explanations as constraints |
DDC Classification: | 004 Computer science |
RVK Classification: | ST 302 |
Peer Reviewed: | Ja |
International Distribution: | Ja |
Type: | Article |
URI: | https://fis.uni-bamberg.de/handle/uniba/47120 |
Release Date: | 30. January 2020 |
Project: | Transparenter Begleiter für medizinische Anwendung |

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University of Bamberg
University of Bamberg