<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  
    
  <dc:date>2020-01</dc:date>
    
  
    
  <dc:identifier>https://phaidra.ustp.at/o:4336</dc:identifier>
    
  
    
  <dc:description xml:lang="eng">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).</dc:description>
    
  
    
  <dc:source>ERCIM News(120), 41-42 (2020-01)</dc:source>
    
  
    
  <dc:type xml:lang="eng">article</dc:type>
    
  
    
  <dc:title xml:lang="eng">KANDINSKY Patterns: A Swiss-Knife for the Study of Explainable AI.</dc:title>
    
  
    
  <dc:language>eng</dc:language>
    
  
    
  <dc:format>application/pdf</dc:format>
    
  
    
  <dc:creator>Holzinger, Andreas (Medical University Graz and Alberta Machine Intelligence Institute Edmonton, Canada)</dc:creator>
    
  <dc:creator>Kieseberg, Peter (University of Applied Sciences, St.Poelten)</dc:creator>
    
  <dc:creator>Müller, Heimo (Medical University Graz)</dc:creator>
    
  
    
  <dc:rights>All rights reserved</dc:rights>
    
  
</oai_dc:dc>