  Neuro-symbolic predictors learn a mapping from sub-symbolic inputs to
higher-level concepts and then carry out (probabilistic) logical inference on
this intermediate representation. This setup offers clear advantages in terms
of consistency to symbolic prior knowledge, and is often believed to provide
interpretability benefits in that - by virtue of complying with the knowledge -
the learned concepts can be better understood by human stakeholders. However,
it was recently shown that this setup is affected by reasoning shortcuts
whereby predictions attain high accuracy by leveraging concepts with unintended
semantics, yielding poor out-of-distribution performance and compromising
interpretability. In this short paper, we establish a formal link between
reasoning shortcuts and the optima of the loss function, and identify
situations in which reasoning shortcuts can arise. Based on this, we discuss
limitations of natural mitigation strategies such as reconstruction and concept
supervision.
