Susanne Dandl

According to our database, Susanne Dandl authored at least 9 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

counterfactuals: An R Package for Counterfactual Explanation Methods.

Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio

CoRR, 2023

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations.

Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation.

Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, Giuseppe Casalicchio

Proceedings of the Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30, 2023

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.

Ludwig Bothmann, Susanne Dandl, Michael Schomaker

Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023), 2023

2022

Multi-objective counterfactual fairness.

Susanne Dandl, Florian Pfisterer, Bernd Bischl

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021

mcboost: Multi-Calibration Boosting for R.

Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl

J. Open Source Softw., 2021

2020

Pitfalls to Avoid when Interpreting Machine Learning Models.

Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

CoRR, 2020

Multi-Objective Counterfactual Explanations.

Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

Proceedings of the xxAI - Beyond Explainable AI, 2020