Proposal Description

Machine learning, explainability and statistics
Chair: Nalini Ravishanker, President-elect of the International Statistical Institute, University of Connecticut
Rosanna Verde, Coordinator on the Italian group “Statistics and data science”, Università della Campania Luigi Vanvitelli, Italy, “Explainable prediction model for distribution data "
Dominik Rozkrut, President of Statistics Poland, President of the IAOS, Poland “Explainability in the context of official statistics”
Linda Young, Chief Mathematical Statistician and Director of Research and Development, United States Department of Agriculture, National Agricultural Statistics Service, USA “Using Machine Learning in the Production of Official Statistics: Progress, Potential, and Challenges”
Daniel Jeske, University of California, Riverside, USA. Chair of the ISI Working Group on Data Science “On Combining Estimators with an Application to Randomized Clinical Trials”
Machine learning has a great and increasing importance in several branches of data analysis when using large data sets and new data sources, e.g., administrative registers, satellites and aircrafts, webcams, data voluntarily provided by internet users, data harvested from the web and so on. The applications of machine learning tools rage from earth observation to official statistics, and the discussion on advantages, disadvantages, limitations, and requirements of information extraction through machine learning and alternative data sources is informing the debate all over the world.
This Invited Paper Session (IPS) focuses on most relevant methodological and applied issues of machine learning, with particular attention to interpretability and potential bias.
This IPS is balanced from geographical and gender point of view and is proposed by the ISI Special Interest Group on Data Science.