First AI for Drinking Water Chlorination Challenge

Artelt A, Hermes L, Strotherm J, Hammer B, Vrachimis SG, Kyriakou MS, Eliades DG, Polycarpou MM, Paraskevopoulos S, Vrochidis S, Taormina R, et al. (2026)
Journal of Water Resources Planning and Management 152(12).

Hu-CEMNIST: A Benchmark Dataset of Human-Generated Counterfactual Explanations for MNIST

Kuhl U, Artelt A (2026)
In: Proceedings of the IJCAI–ECAI 2026 Workshop on Explainable Artificial Intelligence (XAI). Baier H, Huber T, Sreedharan S, Weitz K, Vasileiou SL (Eds); 179-202.

Interpretable event diagnosis in water distribution networks

Artelt A, Vrachimis SG, Eliades DG, Kuhl U, Hammer B, Polycarpou MM (2026)
Intelligent Systems with Applications 29: 200621.

Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals

Mazur A, Peters H, Artelt A, Koller L, Hartmann C, Trächtler A, Hammer B (2026)
In: Artificial Neural Networks and Machine Learning – ICANN 2025, PT IV. Lecture Notes in Computer Science, 16071. Cham: Springer : 192-203.

The effect of data poisoning on counterfactual explanations

Artelt A, Sharma S, Lecué F, Hammer B (2026)
Information Fusion 132: 104237.

A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks

Hermes L, Artelt A, Vrachimis SG, Polycarpou MM, Hammer B (2025)
SN Computer Science 6(5): 522.

Explainable AI, energy and critical infrastructure systems

Leofante F, Artelt A, Eliades D, Korre A, Toni F, Miller T (2025)
AI Magazine 46(3): e70033.

Neural Surrogate Model in an Extended Kalman Filter for Chlorine Concentration State Esimation in Water Distribution Systems

Artelt A, Strotherm J, Hermes L, Hammer B (2025)
In: 2025 6th International Conference on Control and Fault-Tolerant Systems (SysTol). Conference on Control and Fault-Tolerant Systems. New York: IEEE: 277-283.

Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks

Vaquet V, Hinder F, Artelt A, Ashraf I, Strotherm J, Vaquet J, Brinkrolf J, Hammer B (2024)
In: Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX. Wand M, Malinovská K, Schmidhuber J, Tetko IV (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 155-170.

Let Me Explain What I Did or What I Would Have Done: An Empirical Study on the Effects of Explanations and Person-Likeness on Trust in and Understanding of Algorithms

Szczuka JM, Horstmann AC, Szymczyk N, Strathmann C, Artelt A, Mavrina L, Krämer N (2024)
In: NordiCHI '24: Proceedings of the 13th Nordic Conference on Human-Computer Interaction. Association for Computing Machinery (Ed); New York, USA.

EPyT-Flow: A Toolkit for Generating Water DistributionNetwork Data

Artelt A, Kyriakou MS, Vrachimis SG, Eliades DG, Hammer B, Polycarpou MM (2024)
Journal of Open Source Software 9(103): 7104.

Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations

Artelt A, Gregoriades A (2024)
Decision Support Systems 182: 114249.

Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications

Artelt A (2024)
Bielefeld: Universität Bielefeld.

Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams

Jakob J, Artelt A, Hasenjäger M, Hammer B (2023)
Applied Artificial Intelligence 37(1): 2198846.

"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction

Artelt A, Schulz A, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 27-38.

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Kuhl U, Artelt A, Hammer B (2023)
Frontiers in Computer Science 5: 1087929.

Spatial Graph Convolution Neural Networks for Water Distribution Systems

Ashraf MI, Hermes L, Artelt A, Hammer B (2023)
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. Crémilleux B, Hess S, Nijssen S (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 29-41.

For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI

Kuhl U, Artelt A, Hammer B (2023)
In: Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III. Longo L (Ed); Communications in Computer and Information Science. Cham: Springer Nature Switzerland: 280-300.

Unsupervised Unlearning of Concept Drift with Autoencoders

Artelt A, Malialis K, Panayiotou CG, Polycarpou MM, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 703-710.

"I do not know! but why?"- Local model-agnostic example-based explanations of reject

Artelt A, Visser R, Hammer B (2023)
Neurocomputing 558: 126722.

Adversarial Attacks on Leakage Detectors in Water Distribution Networks

Stahlhofen P, Artelt A, Hermes L, Hammer B (2023)
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. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 451-463.

Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks

Vaquet V, Artelt A, Brinkrolf J, Hammer B (2022)
Presented at the 31st International Conference on Artificial Neural Networks, Bristol.

Contrasting Explanations for Understanding and Regularizing Model Adaptations

Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2022)
Neural Processing Letters 55: 5273–5297.

Localization of Concept Drift: Identifying the Drifting Datapoints

Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022) .

One Explanation to Rule them All — Ensemble Consistent Explanations

Artelt A, Vrachimis S, Eliades D, Polycarpou M, Hammer B (2022)
ArXiv:2205.08974 .

Explainable Artificial Intelligence for Improved Modeling of Processes

Velioglu R, Göpfert JP, Artelt A, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 313-325.

Localization of Concept Drift: Identifying the Drifting Datapoints

Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022)
In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.

SAM-kNN Regressor for Online Learning in Water Distribution Networks

Jakob J, Artelt A, Hasenjäger M, Hammer B (2022)
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. Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M (Eds); Lecture Notes in Computer Science, 13531. Cham: Springer Nature : 752-762.

Explaining Reject Options of Learning Vector Quantization Classifiers

Artelt A, Brinkrolf J, Visser R, Hammer B (2022)
In: Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications: 249-261.

“Even if …” – Diverse Semifactual Explanations of Reject

Artelt A, Hammer B (2022)
In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Ishibuchi H (Ed); Piscataway, NJ: IEEE: 854-859.

Faire Algorithmen und die Fairness von Erklärungen: Informatische, rechtliche und ethische Perspektiven

Artelt A, Geminn C, Hammer B, Manzeschke A, Mavrina L, Weber C (2022)
DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany.

Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

Kuhl U, Artelt A, Hammer B (2022)
In: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM: 2125-2137.

Evaluating Robustness of Counterfactual Explanations

Artelt A, Vaquet V, Velioglu R, Hinder F, Brinkrolf J, Schilling M, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE: 01-09.

Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven

Szczuka J, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Strathmann C, Szymczyk N, Varonina L, et al. (2021)
Essen: Universität Duisburg-Essen, Universitätsbibliothek.

Convex optimization for actionable & plausible counterfactual explanations

Artelt A, Hammer B (2021)
arXiv: 2105.07630v1.

Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers

Artelt A, Hammer B (2021)
Neurocomputing 470(VSI: ESANN 2020): 304-317.

Contrastive Explanations for Explaining Model Adaptations

Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2021)
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer : 101-112.

Efficient computation of contrastive explanations

Artelt A, Hammer B (2021)
In: 2021 International Joint Conference on Neural Networks (IJCNN). New York: Institute of Electrical and Electronics Engineers (IEEE): 1-9.

Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

Hinder F, Artelt A, Hammer B (2020)
In: Proceedings of the 37th International Conference on Machine Learning.

Efficient computation of counterfactual explanations of LVQ models

Artelt A, Hammer B (2020)
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.

Convex Density Constraints for Computing Plausible Counterfactual Explanations

Artelt A, Hammer B (2020)
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}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.

Kinder als Nutzende smarter Sprachassistenten Spezieller Gestaltungsbedarf zum Schutz von Kindern

Geminn CL, Szczuka J, Weber C, Artelt A, Varonina L (2020)
Datenschutz und Datensicherheit - DuD 44(9): 600-605.

Improving and Evaluating Conversational User Interfaces for Children

Krämer N, Szczuka J, Rossnagel A, Geminn C, Kopp S, Hammer B, Mavrina L, Artelt A, Manzeschke A, Weber C (2020)
In: IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces. New York: Association for Computing Machinery.

Lecture Notes on Applied Optimization

Paaßen B, Artelt A, Hammer B (2019)
Faculty of Technology, Bielefeld University.

CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox

Artelt A (2019)
Bielefeld University.

On the computation of counterfactual explanations - A survey

Artelt A, Hammer B (2019)
arXiv: 1911.07749v1.

Continuous online user authentication based on keystroke dynamics

Artelt A, Jakob J, Vaquet V (2019)
Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany.

KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht

Krämer N, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Szczuka J, Varonina L, Weber C (2019)
Universität Duisburg-Essen, Universitätsbibliothek.

Introduction to Machine Learning - Supplementary notes

Artelt A (2019) .