The utility of using data-dependent priors to control disintegrated mutual information depends on the balance of two effects: On the one hand, I(W;S) ≤ I(W;S|F), and so conditioning never improves a theoretical bound and may make it looser.
i(W ; S) depends on the unknown data distribution and so distribution-independent bounds will often be very loose.