Publications
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Actionable explainable AI (AxAI): a practical example with aggregation functions for adaptive classification and textual explanations for interpretable machine learning
In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decision is difficult and we need another class called “maybe”, which contains a corresponding quantifiable tendency toward one of these two opposites.…
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Multi-omics disease module detection with an explainable Greedy Decision Forest
Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and…
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GNN-SubNet: disease subnetwork detection with explainable graph neural networks
Motivation The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein–drug interaction networks, as well as for cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability and explainability.…
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Machine learning and knowledge extraction to support work safety for smart forest operations
Forestry work is one of the most difficult and dangerous professions in all production areas worldwide – therefore, any kind of occupational safety and any contribution to increasing occupational safety plays a major role, in line with addressing sustainability goal SDG 3 (good health and well-being). Detailed records of occupational accidents and the analysis of…
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Digital transformation in smart farm and forest operations needs human-centered AI: challenges and future directions
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the…
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Interactive explainable AI platform for graph neural networks
Lack of trust in artificial intelligence (AI) models in medicine is still the key blockage in the use of AI as clinical decision support systems (CDSS). Although AI models are already performing excellently in medicine, their black-box nature entails that it is often impossible to understand why a particular decision was made. In the field…
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Digital Transformation in Smart Farm and Forest Operations needs Human‐Centered AI
AI ML DL Page 1 Inaugural Lecture Andreas Holzinger 7.11.2022 1 Andreas Holzinger, Anna Saranti, Alessa Angerschmid, Carl O. Retzlaff, Christoph Gollob, Arne Nothdurft, Sarah Witzmann, Laura Matiz, Christian Kanzian, Franz Holzleitner, Ferdinand Hönigsberger, Martin Kühmaier, Karl Stampfer University of Natural Resources and Life Sciences, Vienna Department of Forest and Soil Sciences Institute of Forest…
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A practical guide on explainable ai techniques applied on biomedical use case applications
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments,…