André Artelt, B. H. (2024a). Analyzing the Influence of Training Samples on Explanations. in IJCAI – Workshop on Explainable Artificial Intelligence (XAI), ed. T. H. Tim Miller Hendrik Baier doi: 10.48550/arXiv.2406.03012.

André Artelt, S. G. V., Marios S. Kyriakou (2024b). A Toolbox for Supporting Research on AI in Water Distribution Networks. in IJCAI – Workshop on AI in Critical Infrastructure, ed. R. G. Felipe Leno da Silva Wencong Su doi: 10.48550/arXiv.2406.02078.

Artelt, A., and Gregoriades, A. (2024a). A two-stage algorithm for cost-efficient multi-instance counterfactual explanations. CoRR abs/2403.01221. doi: 10.48550/arxiv.2403.01221.

Artelt, A. (2024). Contrasting explanations in machine learning. Efficiency, robustness & applications. doi: 10.4119/unibi/2985769.

André Artelt, S. G. V., Marios S. Kyriakou (2024c). EPyT-Flow – EPANET Python Toolkit - Flow. GitHub repository. Available at: https://github.com/WaterFutures/EPyT-Flow.

Horstmann, A., Artelt, A., Geminn, C. L., Hammer, B., Kopp, S., Manzeschke, A., et al. (2024). Konversation mit künstlicher intelligenz: Gewonnene erkenntnisse und künftige herausforderungen. doi: 10.17185/duepublico/81565.

Artelt, A., and Gregoriades, A. (2024b). Supporting organizational decisions on how to improve customer repurchase using multi-instance counterfactual explanations. Decision Support Systems, 114249. doi: 10.1016/j.dss.2024.114249.

Artelt, A., Sharma, S., Lecué, F., and Hammer, B. (2024). The effect of data poisoning on counterfactual explanations. CoRR abs/2402.08290. Available at: http://arxiv.org/abs/2402.08290.

Stahlhofen, P., Artelt, A., Hermes, L., and Hammer, B. (2023). Adversarial attacks on leakage detectors in water distribution networks. in Advances in computational intelligence - 17th international work-conference on artificial neural networks, IWANN 2023, ponta delgada, portugal, june 19-21, 2023, proceedings, part II Lecture notes in computer science., eds. I. Rojas, G. Joya, and A. Català (Springer), 451–463. doi: 10.1007/978-3-031-43078-7\_37.

Artelt, A., and Hammer, B. (2023). “Explain it in the same way!” – model-agnostic group fairness of counterfactual explanations. in IJCAI – Workshop on XAI, ed. H. B. Ofra Amir Tim Miller doi: 10.48550/arXiv.2211.14858.

Kuhl, U., Artelt, A., and Hammer, B. (2023a). For better or worse: The impact of counterfactual explanations’ directionality on user behavior in xAI. in Explainable artificial intelligence - first world conference, xAI 2023, lisbon, portugal, july 26-28, 2023, proceedings, part III Communications in computer and information science., ed. L. Longo (Springer), 280–300. doi: 10.1007/978-3-031-44070-0\_14.

Artelt, A., Geminn, C. L., Hammer, B., Horstmann, A., Krämer Prof. Dr., N., Manzeschke, A., et al. (2023a). Gesundheits-apps und digitale gesundheitsanwendungen (digas): Ethische, rechtliche, psychologische und informatische perspektiven. doi: 10.17185/duepublico/77379.

Artelt, A., and Gregoriades, A. (2023). “How to make them stay?”: Diverse counterfactual explanations of employee attrition. in Proceedings of the 25th international conference on enterprise information systems, ICEIS 2023, volume 1, prague, czech republic, april 24-26, 2023, eds. J. Filipe, M. Smialek, A. Brodsky, and S. Hammoudi (SCITEPRESS), 532–538. doi: 10.5220/0011961300003467.

Artelt, A., Visser, R., and Hammer, B. (2023b). “I do not know! But why?” - local model-agnostic example-based explanations of reject. Neurocomputing 558, 126722. doi: 10.1016/J.NEUCOM.2023.126722.

Jakob, J., Artelt, A., Hasenjäger, M., and Hammer, B. (2023). Interpretable sam-kNN regressor for incremental learning on high-dimensional data streams. Applied Artificial Intelligence 37. doi: 10.1080/08839514.2023.2198846.

Horstmann, A., Krämer Prof. Dr., N., Geminn, C. L., Bile, T., Weber, C., Manzeschke, A., et al. (2023). Kann sich künstliche intelligenz selbst erklären? Wie erklärungen aus rechtswissenschaftlicher und ethischer sicht gestaltet sein sollten und was psychologie und informatik dazu beitragen können. doi: 10.17185/duepublico/77378.

Kuhl, U., Artelt, A., and Hammer, B. (2023b). Let’s go to the alien zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning. Frontiers Comput. Sci. 5. doi: 10.3389/fcomp.2023.1087929.

Ashraf, I., Hermes, L., Artelt, A., and Hammer, B. (2023). Spatial graph convolution neural networks for water distribution systems. in Advances in intelligent data analysis XXI - 21st international symposium on intelligent data analysis, IDA 2023, louvain-la-neuve, belgium, april 12-14, 2023, proceedings Lecture notes in computer science., eds. B. Crémilleux, S. Hess, and S. Nijssen (Springer), 29–41. doi: 10.1007/978-3-031-30047-9\_3.

Artelt, A., Malialis, K., Panayiotou, C. G., Polycarpou, M. M., and Hammer, B. (2023c). Unsupervised unlearning of concept drift with autoencoders. in 2023 ieee symposium series on computational intelligence (ssci), 703–710. doi: 10.1109/SSCI52147.2023.10372001.

Artelt, A., Schulz, A., and Hammer, B. (2023d). “Why here and not there?”: Diverse contrasting explanations of dimensionality reduction. in Proceedings of the 12th international conference on pattern recognition applications and methods, ICPRAM 2023, lisbon, portugal, february 22-24, 2023, eds. M. D. Marsico, G. S. di Baja, and A. L. N. Fred (SCITEPRESS), 27–38. doi: 10.5220/0011618300003411.

Hinder, F., Artelt, A., Vaquet, V., and Hammer, B. (2022a). Contrasting explanation of concept drift. in 30th european symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2022, bruges, belgium, october 5-7, 2022 doi: 10.14428/esann/2022.ES2022-71.

Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., and Hammer, B. (2022a). Contrasting explanations for understanding and regularizing model adaptations. Neural Processing Letters, 1–25. doi: 10.1007/s11063-022-10826-5.

Artelt, A., and Hammer, B. (2022a). Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers. Neurocomputing 470, 304–317. doi: 10.1016/j.neucom.2021.04.129.

Artelt, A., and Hammer, B. (2022b). “Even if …” - diverse semifactual explanations of reject. in IEEE symposium series on computational intelligence, SSCI 2022, singapore, december 4-7, 2022, eds. H. Ishibuchi, C. Kwoh, A. Tan, D. Srinivasan, C. Miao, A. Trivedi, et al. (IEEE), 854–859. doi: 10.1109/SSCI51031.2022.10022139.

Velioglu, R., Göpfert, J. P., Artelt, A., and Hammer, B. (2022). Explainable artificial intelligence for improved modeling of processes. in Intelligent data engineering and automated learning - IDEAL 2022 - 23rd international conference, IDEAL 2022, manchester, uk, november 24-26, 2022, proceedings Lecture notes in computer science., eds. H. Yin, D. Camacho, and P. Tiño (Springer), 313–325. doi: 10.1007/978-3-031-21753-1\_31.

Artelt, A., Brinkrolf, J., Visser, R., and Hammer, B. (2022b). Explaining reject options of learning vector quantization classifiers. in Proceedings of the 14th international joint conference on computational intelligence, IJCCI 2022, valletta, malta, october 24-26, 2022, eds. T. Bäck, B. van Stein, C. Wagner, J. M. Garibaldi, H. K. Lam, M. Cottrell, et al. (SCITEPRESS), 249–261. doi: 10.5220/0011389600003332.

Artelt, A., Geminn, C., Hammer, B., Manzeschke, A., Mavrina, L., and Weber, C. (2022c). Faire algorithmen und die fairness von erklärungen: Informatische, rechtliche und ethische perspektiven. doi: 10.17185/duepublico/76311.

Mazur, A., Artelt, A., and Hammer, B. (2022). Improving zorro explanations for sparse observations with dense proxy data. in 30th european symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2022, bruges, belgium, october 5-7, 2022 doi: 10.14428/esann/2022.ES2022-27.

Kuhl, U., Artelt, A., and Hammer, B. (2022). Keep your friends close and your counterfactuals closer: Improved learning from closest rather than plausible counterfactual explanations in an abstract setting. in FAccT ’22: 2022 ACM conference on fairness, accountability, and transparency, seoul, republic of korea, june 21 - 24, 2022 (ACM), 2125–2137. doi: 10.1145/3531146.3534630.

Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., and Hammer, B. (2022b). Localization of concept drift: Identifying the drifting datapoints. in International joint conference on neural networks, IJCNN 2022, padua, italy, july 18-23, 2022 (IEEE), 1–9. doi: 10.1109/IJCNN55064.2022.9892374.

Artelt, A., Visser, R., and Hammer, B. (2022d). Model agnostic local explanations of reject. in 30th european symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2022, bruges, belgium, october 5-7, 2022 doi: 10.14428/esann/2022.ES2022-34.

Artelt, A., Vrachimis, S., Eliades, D., Polycarpou, M., and Hammer, B. (2022e). One explanation to rule them all – ensemble consistent explanations. in IJCAI – Workshop on XAI, eds. R. Weber, O. Amir, and T. Miller doi: 10.48550/arXiv.2205.08974.

Hinder, F., Artelt, A., Vaquet, V., and Hammer, B. (2022c). Precise change point detection using spectral drift detection. CoRR abs/2205.06507. doi: 10.48550/arXiv.2205.06507.

Jakob, J., Artelt, A., Hasenjäger, M., and Hammer, B. (2022). SAM-kNN regressor for online learning in water distribution networks. in Artificial neural networks and machine learning - ICANN 2022 - 31st international conference on artificial neural networks, bristol, uk, september 6-9, 2022, proceedings, part III Lecture notes in computer science., eds. E. Pimenidis, P. P. Angelov, C. Jayne, A. Papaleonidas, and M. Aydin (Springer), 752–762. doi: 10.1007/978-3-031-15934-3\_62.

Vaquet, V., Artelt, A., Brinkrolf, J., and Hammer, B. (2022). Taking care of our drinking water: Dealing with sensor faults in water distribution networks. in Artificial neural networks and machine learning - ICANN 2022 - 31st international conference on artificial neural networks, bristol, uk, september 6-9, 2022, proceedings, part II Lecture notes in computer science., eds. E. Pimenidis, P. P. Angelov, C. Jayne, A. Papaleonidas, and M. Aydin (Springer), 682–693. doi: 10.1007/978-3-031-15931-2\_56.

Artelt, A., Malialis, K., Panayiotou, C. G., Polycarpou, M. M., and Hammer, B. (2022f). Unsupervised unlearning of concept drift with autoencoders. CoRR abs/2211.12989. doi: 10.48550/arXiv.2211.12989.

Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., and Hammer, B. (2021a). Contrastive explanations for explaining model adaptations. in Advances in computational intelligence - 16th international work-conference on artificial neural networks, IWANN 2021, virtual event, june 16-18, 2021, proceedings, part I Lecture notes in computer science., eds. I. Rojas, G. Joya, and A. Català (Springer), 101–112. doi: 10.1007/978-3-030-85030-2\_9.

Artelt, A., and Hammer, B. (2021a). Convex optimization for actionable \& plausible counterfactual explanations. CoRR abs/2105.07630. Available at: https://arxiv.org/abs/2105.07630.

Artelt, A., and Hammer, B. (2021b). Efficient computation of contrastive explanations. in International joint conference on neural networks, IJCNN 2021, shenzhen, china, july 18-22, 2021 (IEEE), 1–9. doi: 10.1109/IJCNN52387.2021.9534454.

Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., et al. (2021b). Evaluating robustness of counterfactual explanations. in IEEE symposium series on computational intelligence, SSCI 2021, orlando, fl, usa, december 5-7, 2021 (IEEE), 1–9. doi: 10.1109/SSCI50451.2021.9660058.

Szczuka, J., Artelt, A., Geminn, C., Hammer, B., Kopp Prof. Dr., S., Manzeschke, A., et al. (2021). Können kinder aufgeklärte nutzer*innen von sprachassistenten sein? Rechtliche, psychologische, ethische und informatische perspektiven. doi: 10.17185/duepublico/74238.

Göpfert, J. P., Artelt, A., Wersing, H., and Hammer, B. (2020). Adversarial attacks hidden in plain sight. in Advances in intelligent data analysis XVIII - 18th international symposium on intelligent data analysis, IDA 2020, konstanz, germany, april 27-29, 2020, proceedings Lecture notes in computer science., eds. M. R. Berthold, A. Feelders, and G. Krempl (Springer), 235–247. doi: 10.1007/978-3-030-44584-3\_19.

Artelt, A., and Hammer, B. (2020a). Convex density constraints for computing plausible counterfactual explanations. in Artificial neural networks and machine learning - ICANN 2020 - 29th international conference on artificial neural networks, bratislava, slovakia, september 15-18, 2020, proceedings, part I Lecture notes in computer science., eds. I. Farkas, P. Masulli, and S. Wermter (Springer), 353–365. doi: 10.1007/978-3-030-61609-0\_28.

Artelt, A., and Hammer, B. (2020b). Efficient computation of counterfactual explanations of LVQ models. in 28th european symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2020, bruges, belgium, october 2-4, 2020, 19–24. Available at: https://www.esann.org/sites/default/files/proceedings/2020/ES2020-55.pdf.

Geminn, C. L., Szczuka, J. M., Weber, C., Artelt, A., and Varonina, L. (2020). Kinder als nutzende smarter sprachassistenten. Datenschutz und Datensicherheit 44, 606–610. doi: 10.1007/s11623-020-1333-x.

Hinder, F., Artelt, A., and Hammer, B. (2020). Towards non-parametric drift detection via dynamic adapting window independence drift detection (DAWIDD). in Proceedings of the 37th international conference on machine learning, ICML 2020, 13-18 july 2020, virtual event Proceedings of machine learning research. (PMLR), 4249–4259. Available at: http://proceedings.mlr.press/v119/hinder20a.html.

Hinder, F., Artelt, A., and Hammer, B. (2019). A probability theoretic approach to drifting data in continuous time domains. CoRR abs/1912.01969. Available at: http://arxiv.org/abs/1912.01969.

Artelt, A. (2019a). CEML: Counterfactuals for Explaining Machine Learning models - A Python toolbox. Available at: https://github.com/andreArtelt/ceml.

Artelt, A. (2019b). Introduction to machine learning-supplementary notes. Available at: http://nbn-resolving.de/urn:nbn:de:0070-pub-29364081.

Krämer Prof. Dr., N., Artelt, A., Geminn, C., Hammer, B., Kopp Prof. Dr., S., Manzeschke, A., et al. (2019). KI-basierte sprachassistenten im alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher sicht. doi: 10.17185/duepublico/70571.

Paaßen, B., Artelt, A., and Hammer, B. (2019). Lecture notes on applied optimization. Available at: http://nbn-resolving.de/urn:nbn:de:0070-pub-29352007.

Artelt, A., and Hammer, B. (2019). On the computation of counterfactual explanations - A survey. CoRR abs/1911.07749. Available at: http://arxiv.org/abs/1911.07749.