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

Invited talks

  • 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

Papers in conference proceedings



  • Kliegr, Tomáš. Effect of cognitive biases on human understanding of rule-based machine learning models. Queen Mary University of London, 2017. Ph.D. Dissertation

Media interviews

Other contributions

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) .