1. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2. André Artelt . 2019 . CEML: Counterfactuals for Explaining Machine Learning models - A Python toolbox. https://www.github.com/andreArtelt/ceml Publication Title: GitHub repository. André Artelt. 2019. CEML: Counterfactuals for Explaining Machine Learning models - A Python toolbox. https://www.github.com/andreArtelt/ceml Publication Title: GitHub repository.
3. André Artelt and Barbara Hammer. 2019. On the computation of counterfactual explanations - A survey. CoRR abs/1911.07749(2019). http://arxiv.org/abs/1911.07749 _eprint: 1911.07749. André Artelt and Barbara Hammer. 2019. On the computation of counterfactual explanations - A survey. CoRR abs/1911.07749(2019). http://arxiv.org/abs/1911.07749 _eprint: 1911.07749.
4. Convex Density Constraints for Computing Plausible Counterfactual Explanations
5. André Artelt and Barbara Hammer . 2022. Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers. Neurocomputing 470 (Jan . 2022 ), 304–317. https://doi.org/10.1016/j.neucom.2021.04.129 10.1016/j.neucom.2021.04.129 André Artelt and Barbara Hammer. 2022. Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers. Neurocomputing 470 (Jan. 2022), 304–317. https://doi.org/10.1016/j.neucom.2021.04.129