  We describe recent research on the use of actual causality in the definition
of responsibility scores as explanations for query answers in databases, and
for outcomes from classification models in machine learning. In the case of
databases, useful connections with database repairs are illustrated and
exploited. Repairs are also used to give a quantitative measure of the
consistency of a database. For classification models, the responsibility score
is properly extended and illustrated. The efficient computation of Shap-score
is also analyzed and discussed. The emphasis is placed on work done by the
author and collaborators.
