  We study the general properties of robust convex risk measures as worst-case
values under uncertainty on random variables. We establish general concrete
results regarding convex conjugates and sub-differentials. We refine some
results for closed forms of worstcase law invariant convex risk measures under
two concrete cases of uncertainty sets for random variables: based on the first
two moments and Wasserstein balls.
