Modern machine learning models can be so flexible that multiple parameter configurations — sometimes with diverging properties — can explain the observed data equally well. This "underspecification" leads to unpredictable and sometimes undesirable behavior. We review examples of underspecified pipelines and their consequences in a variety of domains. We survey approaches to resolve this problem, which typically introduce an inductive bias to constrain the solution space and better control model behavior. We compare these techniques, with a particular focus on hybrid models — approaches that blend highly structured models (i.e., built from first principles) with flexible pattern recognition components (i.e., deep, data-driven) to produce expressive models with sensible generalization behavior.