  We discuss the discontinuities that arise when mapping unordered objects to
neural network outputs of fixed permutation, referred to as the responsibility
problem. Prior work has proved the existence of the issue by identifying a
single discontinuity. Here, we show that discontinuities under such models are
uncountably infinite, motivating further research into neural networks for
unordered data.
