641 citations · 720 across the 6 of their papers we have counts for
5 papers · 1 filter
KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence
Andreas Holzinger, Anna Saranti, Heimo Mueller
Machine intelligence is very successful at standard recognition tasks when having high-quality training data. There is still a significant gap between machine-level pattern recogni…
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations
Andreas Holzinger, André Carrington, Heimo Müller
Recent success in Artificial Intelligence (AI) and Machine Learning (ML) allow problem solving automatically without any human intervention. Autonomous approaches can be very conve…
The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations
Bernd Malle, Nicola Giuliani, Peter Kieseberg +1
AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applicatio…
What do we need to build explainable AI systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis +1
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous dri…
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Andreas Holzinger, Bernd Malle, Peter Kieseberg +4
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the…