Andreas HOLZINGER

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Full Professor for digital transformation in smart farm and forest operations

University of Natural Resources and Life Sciences

Austria

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Andreas Holzinger is a Full Professor for Digital Transformation in Smart Farm and Forest Operations at the University of Natural Resources and Life Sciences, Austria. His research interests include artificial intelligence applications in agriculture and forestry, interpretability in machine learning, and enhancing trust in automated data interpretation. Recent publications focus on the integration of large language models and explainable AI for applications in various domains, including agriculture, forestry, and information security.

Recent Grants

Grant: Close

EcosysteM for Pathology Diagnostics with AI Assistance (EMPAIA)

Open Date: 2020-01-01

Close Date: 2023-09-01

Grant: Close

OPENQKD

Open Date: 2019-12-01

Close Date: 2023-12-01

Grant: Close

EXPLAINABLE-AI - A reference model of explainable Artificial Intelligence for the Medical Domain

Open Date: 2019-10-01

Close Date: 2022-10-01

Grant: Close

FeatureCloud - Privacy preserving federated machine learning and blockchaining for reduced cyber risks in a world of distributed healthcare

Open Date: 2019-01-01

Close Date: 2023-01-01

Grant: Close

BIMDANUBE Network of Excellence on Biomedical Information Management in the Danube Region

Open Date: 2017-07-01

Close Date: 2019-02-01

Andreas HOLZINGER

Research Interests

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Recent Grants

Articles (30)

What can artificial intelligence do for soil health in agriculture?

ForestGPT and Beyond: A Trustworthy Domain-Specific Large Language Model Paving the Way to Forestry 5.0

Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactuals

On the disagreement problem in Human-in-the-Loop federated machine learning

Gamifying information security: Adversarial risk exploration for IT/OT infrastructures

Grand Challenges of Smart Technology for Older Adults

Integrating Belief-Desire-Intention agents with large language models for reliable human–robot interaction and explainable Artificial Intelligence

NiaAML: AutoML for classification and regression pipelines

Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning

Fine-tuning language model embeddings to reveal domain knowledge: An explainable artificial intelligence perspective on medical decision making

From 3D point‐cloud data to explainable geometric deep learning: State‐of‐the‐art and future challenges

Ethical ChatGPT: Concerns, Challenges, and Commandments

Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks

Sensors for Digital Transformation in Smart Forestry

Exploring artificial intelligence for applications of drones in forest ecology and management

On generating trustworthy counterfactual explanations

The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations

Special Issue “Selected Papers from CD-MAKE 2020 and ARES 2020”

Explainability and causability in digital pathology

Toward human-level concept learning: Pattern benchmarking for AI algorithms

Digital Transformation Needs Trustworthy Artificial Intelligence

The Cost of Understanding—XAI Algorithms towards Sustainable ML in the View of Computational Cost

Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI

Understanding and Explaining Diagnostic Paths: Toward Augmented Decision Making

The explainability paradox: Challenges for xAI in digital pathology

Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning

Interpretable Machine Learning with Brain Image and Survival Data

Collaborators (17)

Constantine Stephanidis

Markus Loecher

Peter Kieseberg

Ugo PAGALLO

Iztok Fister

Sancho Salcedo-Sanz

Fleur Jeanquartier

Anne-Christin Hauschild

Karl Stampfer

Karl Stöger

Michael Friebe

Javier Del Ser

Sara Czaja

Gianclaudio Malgieri

Norman Zerbe

Claire Jean-Quartier

Heimo Müller

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