Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization. - Nadia Burkart; Marco F. Huber; Phillip Faller (ICMLA, 2019)

Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain. - Manjunatha Veerappa; Mathias Anneken; Nadia Burkart (ICPR Workshops (3), 2020)

Batch-wise Regularization of Deep Neural Networks for Interpretability. - Nadia Burkart; Philipp Michael Faller; Elisabeth Peinsipp; Marco F. Huber (MFI, 2020)

Supported Decision-Making by Explainable Predictions of Ship Trajectories. - Nadia Burkart; Marco F. Huber; Mathias Anneken (SOCO, 2020)

A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search. - Maximilian Becker; Nadia Burkart; Pascal Birnstill; Jürgen Beyerer (Adv. Artif. Intell. Mach. Learn., 2021)

Are you sure? Prediction revision in automated decision-making. - Nadia Burkart; Sebastian Robert; Marco F. Huber (Expert Syst. J. Knowl. Eng., 2021)

A Survey on the Explainability of Supervised Machine Learning. - Nadia Burkart; Marco F. Huber (J. Artif. Intell. Res., 2021)

Explainable AI: introducing trust and comprehensibility to AI engineering. - Nadia Burkart; Danilo Brajovic; Marco F. Huber (Autom., 2022)

Validation of XAI explanations for multivariate time series classification in the maritime domain. - Manjunatha Veerappa; Mathias Anneken; Nadia Burkart; Marco F. Huber (J. Comput. Sci., 2022)

Erklärbare Künstliche Intelligenz - Steigerung der Nachvollziehbarkeit überwachter maschineller Lernverfahren. - Nadia Burkart (2022)

Ethical Considerations for the Military Use of Artificial Intelligence in Visual Reconnaissance. - Mathias Anneken; Nadia Burkart; Fabian Jeschke; Achim Kuwertz-Wolf; Almuth Mueller; Arne Schumann; Michael Teutsch (CoRR, 2025)