Explainable machine learning models
This page provides a list of my papers (and other contributions) related to explainable machine learning:
Software for explainable machine learning
- EasyMiner.eu (project lead)
- Assocation rule classification arc R package (creator)
- Assocation rule classification rCBA R package (contributor)
- Explanation of association rule models (contributor)
- Software created within master theses that I supervise or have supervised: action rule discovery in Python (ActionRules, RandomForestRules), Assocation rule classification in Python (pyARC), Interpretable Decision Sets in Python (pyIDS).
- Tutorial on Explainable machine learning for FORTISS (Research institute of the Free State of Bavaria for software-intensive systems and services) research center, Munich, 2020
Articles in scientific journals
- Kliegr, Tomáš, Štěpán Bahník, and Johannes Fürnkranz. "Advances in Machine Learning for the Behavioral Sciences." American Behavioral Scientist 64.2 (2020): 145-175.
- Fürnkranz, J., Kliegr, T., & Paulheim, H. (2019). On cognitive preferences and the plausibility of rule-based models. Machine Learning, Springer, 1-46.
- Vojíř, S., Zeman, V., Kuchař, J., & Kliegr, T. (2018). EasyMiner. eu: Web framework for interpretable machine learning based on rules and frequent itemsets. Knowledge-Based Systems, 150, 111-115.
- Kliegr, T., Svátek, V., Ralbovský, M., & Šimůnek, M. (2011). SEWEBAR-CMS: semantic analytical report authoring for data mining results. Journal of Intelligent Information Systems, 37(3), 371-395.
Papers in conference proceedings
- Filip, Jiri, and Tomáš Kliegr. "PyIDS–Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016⋆." RuleML Challenge, CEUR-WS (2019). Best paper award at RuleML Challenge 2019.
- Johannes Fürnkranz, and Tomáš Kliegr. "The Need for Interpretability Biases." International Symposium on Intelligent Data Analysis. Springer, Cham, 2018.
- Genský, Oliver, Žárský, Jiří, Kliegr, Tomáš. Empirical Evaluation of Explainability of Topic modelling and Clustering Visualizations. Znalosti 2019.
- Tomáš Kliegr, Štěpán Bahník, and Johannes Fürnkranz. "A review of possible effects of cognitive biases on interpretation of rule-based machine learning models." arXiv preprint arXiv:1804.02969 (2018). Under revision in Artificial Intelligence (Elsevier)
- Tomáš Kliegr. "QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules." arXiv preprint arXiv:1711.10166 (2017).
- Kliegr, Tomáš. Effect of cognitive biases on human understanding of rule-based machine learning models. Queen Mary University of London, 2017. Ph.D. Dissertation
- Can AI be free of bias?, interview featured in an article of German broadcaster Deutsche Welle.
I serve as a program co-chair of RuleML+RR 2020@DeclarativeAI (originally to be held in Oslo). The theme of the conference is Explainable algorithmic decision-making.
I serve or have served as a reviewer specializing on topics related to explainable machine learning at multiple artificial intelligence and semantic web conferences, such as AAAI, ECAI, IJCAI, ECML/PKDD, ISWC, ... (cf. academic service for a list) .