  In this concept paper, we discuss intricacies of specifying and verifying the
quality of continuous and lifelong learning artificial intelligence systems as
they interact with and influence their environment causing a so-called concept
drift. We signify a problem of implicit feedback loops, demonstrate how they
intervene with user behavior on an exemplary housing prices prediction system.
Based on a preliminary model, we highlight conditions when such feedback loops
arise and discuss possible solution approaches.
