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2020 – today
- 2025
[i2]Mathias Anneken, Nadia Burkart, Fabian Jeschke, Achim Kuwertz-Wolf, Almuth Mueller, Arne Schumann, Michael Teutsch:
Ethical Considerations for the Military Use of Artificial Intelligence in Visual Reconnaissance. CoRR abs/2502.03376 (2025)- 2022
- [b1]Nadia Burkart:
Erklärbare Künstliche Intelligenz - Steigerung der Nachvollziehbarkeit überwachter maschineller Lernverfahren. Karlsruhe Institute of Technology, Germany, 2022 - [j5]Nadia Burkart, Danilo Brajovic, Marco F. Huber:
Explainable AI: introducing trust and comprehensibility to AI engineering. Autom. 70(9): 787-792 (2022) - [j4]Manjunatha Veerappa
, Mathias Anneken, Nadia Burkart, Marco F. Huber:
Validation of XAI explanations for multivariate time series classification in the maritime domain. J. Comput. Sci. 58: 101539 (2022) - 2021
- [j3]Maximilian Becker, Nadia Burkart, Pascal Birnstill, Jürgen Beyerer:
A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search. Adv. Artif. Intell. Mach. Learn. 1(2): 90-110 (2021) - [j2]Nadia Burkart, Sebastian Robert, Marco F. Huber:
Are you sure? Prediction revision in automated decision-making. Expert Syst. J. Knowl. Eng. 38(1) (2021) - [j1]Nadia Burkart, Marco F. Huber:
A Survey on the Explainability of Supervised Machine Learning. J. Artif. Intell. Res. 70: 245-317 (2021) - 2020
- [c4]Manjunatha Veerappa, Mathias Anneken, Nadia Burkart:
Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain. ICPR Workshops (3) 2020: 204-218 - [c3]Nadia Burkart, Philipp Michael Faller, Elisabeth Peinsipp, Marco F. Huber:
Batch-wise Regularization of Deep Neural Networks for Interpretability. MFI 2020: 216-222 - [c2]Nadia Burkart, Marco F. Huber, Mathias Anneken:
Supported Decision-Making by Explainable Predictions of Ship Trajectories. SOCO 2020: 44-54 - [i1]Nadia Burkart, Marco F. Huber:
A Survey on the Explainability of Supervised Machine Learning. CoRR abs/2011.07876 (2020)
2010 – 2019
- 2019
- [c1]Nadia Burkart, Marco F. Huber, Phillip Faller:
Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization. ICMLA 2019: 700-705
Coauthor Index
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