Alessa Angerschmid

Orcid: 0000-0001-9209-6676

According to our database1, Alessa Angerschmid authored at least 14 papers between 2022 and 2025.

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Bibliography

2025
Correction: Generating Explanations for Conceptual Validation of Graph Neural Networks.
Künstliche Intell., December, 2025

2024
col-LAUT - Colorized point clouds of marked trees collected with Terrestrial and Personal laser scanning from Austrian forest stands.
Dataset, February, 2024

CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks.
J. Biomed. Informatics, 2024

Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities.
J. Artif. Intell. Res., 2024

Automatic detection of color markings and numbers on trees in point clouds from Personal Laser Scanning (PLS) and Terrestrial Laser Scanning (TLS).
Ecol. Informatics, 2024

Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists.
Cogn. Syst. Res., 2024

Assessing trustworthy AI: Technical and legal perspectives of fairness in AI.
Comput. Law Secur. Rev., 2024

2023
Toward human-level concept learning: Pattern benchmarking for AI algorithms.
Patterns, August, 2023

2022
Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning.
Mach. Learn. Knowl. Extr., December, 2022

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions.
Sensors, 2022

Fairness and Explanation in AI-Informed Decision Making.
Mach. Learn. Knowl. Extr., 2022

Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs.
Künstliche Intell., 2022

Machine Learning and Knowledge Extraction to Support Work Safety for Smart Forest Operations.
Proceedings of the Machine Learning and Knowledge Extraction, 2022

Effects of Fairness and Explanation on Trust in Ethical AI.
Proceedings of the Machine Learning and Knowledge Extraction, 2022


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