Florian Dumpert heads a division at the Federal Statistical Office of Germany that develops methodological and technological solutions and architectures for statistics production. The focus of his work is on the quality-assured integration and use of machine learning for the purpose of digitalisation, standardisation and automation of official statistics. His research interests include statistical machine learning, statistical data processing and imputation. He regularly participates in national and international projects on these topics and represents the disciplines in relevant working groups and committees.

• Introduction.- 1. ML in official statistics (T Augustin, AL Boulesteix - LMU Munich).- 2. Evaluation of generalization error (B Bischl, AL Boulesteix, R Hornung, H Kümpel, S Fischer, A Bender, L Bothman, L Schneider -- LMU Munich).- 3. ML and Design of Experiments/Sample size calculation (T Augustin - LMU Munich).- 4. Interpretable ML (B Bischl, L Bothmann, S Dandl, G Casalicchio -- LMU Munich).- 5. Set-valued methods for ML in official statistics (T Augustin - LMU Munich).- 6. Ethics and Fairness (F Kreuter - at LMU Munich).- 7. Quality aspects of ML (Y Saidani et al -- Statistical Offices in Germany).- 8. A statistical matching pipeline (T Küntzler --- Destatis).- 9. Legal Aspects of ML (T Fetzer - Mannheim University).