  A recent body of work addresses safety constraints in explore-and-exploit
systems. Such constraints arise where, for example, exploration is carried out
by individuals whose welfare should be balanced with overall welfare. In this
paper, we adopt a model inspired by recent work on a bandit-like setting for
recommendations. We contribute to this line of literature by introducing a
safety constraint that should be respected in every round and determines that
the expected value in each round is above a given threshold. Due to our
modeling, the safe explore-and-exploit policy deserves careful planning, or
otherwise, it will lead to sub-optimal welfare. We devise an asymptotically
optimal algorithm for the setting and analyze its instance-dependent
convergence rate.
