Consider an online financial advisor (robo-advisor) that makes investment decisions tailored to the risk-preferences of a customer. As time goes by, the customer’s risk-preferences change, and when the uncertainty is high enough, the machine become unsure about what investment decision to make, and pings the customer to ask for updated information.

Can this be viewed as an active learning situation?

In active learning one strategically decides which samples to label, based on the information it provides. Here there is really just one “data point”, the customer, that is “labeled” to begin with. The active learning analogy would be that when the machine becomes unsure about the label (risk-preferences), it asks for re-labeling (updated risk-preferences), i.e., it re-labels at a point where it provides a lot of information.