Kacper is an Affiliate at the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) from the Medical Data Science group at ETH Zurich.
Kacper’s main research focus is transparency – interpretability and explainability – of data-driven predictive systems based on artificial intelligence and machine learning algorithms. In particular, he has done work on enhancing transparency of predictive models with feasible and actionable counterfactual explanations and robust modular surrogate explainers. He has also introduced Explainability Fact Sheets – a comprehensive taxonomy of AI and ML explainers – and prototyped dialogue-driven interactive explainability systems.
Kacper is the designer and lead developer of FAT Forensics – an open source fairness, accountability and transparency Python toolkit. Additionally, he is the main author of a collection of online interactive training materials about machine learning explainability, created in collaboration with the Alan Turing Institute – the UK’s national institute for data science and artificial intelligence.
Kacper holds a Master’s degree in Mathematics and Computer Science and a doctorate in Computer Science from the University of Bristol, United Kingdom. Prior to joining the Medical Data Science Group at ETH Zurich he was a Research Fellow in the Centre, and before that he has held numerous research posts at the University of Bristol, working with projects such as REFrAMe, SPHERE and European Union’s AI Research Excellence Centre TAILOR.