<resource xmlns:datacite="http://datacite.org/schema/kernel-4">
<creators>
<creator>
<creatorName>Holzinger, Andreas (Medical University Graz and Alberta Machine Intelligence Institute Edmonton, Canada)</creatorName>
<givenName>Andreas</givenName>
<familyName>Holzinger</familyName>
</creator>
<creator>
<creatorName>Kieseberg, Peter (University of Applied Sciences, St.Poelten)</creatorName>
<givenName>Peter</givenName>
<familyName>Kieseberg</familyName>
</creator>
<creator>
<creatorName>Müller, Heimo (Medical University Graz)</creatorName>
<givenName>Heimo</givenName>
<familyName>Müller</familyName>
</creator>
</creators>
<titles>
<title>KANDINSKY Patterns: A Swiss-Knife for the Study of Explainable AI.</title>
</titles>
<publicationYear>2020-01</publicationYear>
<descriptions>
<description descriptionType="Other">Kandinsky Patterns are mathematically describable,
simple, self-contained, hence controllable test datasets
for the development, validation and training of
explainability in artificial intelligence (AI) and machine
learning (ML).</description>
</descriptions>
<resourceType resourceTypeGeneral="Text">PDFDocument</resourceType>
<language>en</language>
<dates>
<date dateType="Created">2020-12-15T08:35:12.442Z</date>
</dates>
<sizes>
<size>772106 b</size>
</sizes>
<formats>
<format>application/pdf</format>
</formats>
<rightsList>
<rights>All rights reserved</rights>
</rightsList>
</resource>
