Model interpretability methods provide an understanding of complex model decisions and verify that a meaningful difference in the data has been identified. We have applied model interpretability methods to our predictive model of genome truncation within adeno-associated virus (AAV) manufacturing and revealed that the model uses a set of DNA secondary structures predictive of truncation. These secondary structures provide a simple mechanism for understanding AAV truncations and a strong basis for independently validating our model’s predictions. Moreover, these structures have been well studied and are shown to be related to DNA replication errors; however, only one of these structures (i.e., hairpins) has been previously implicated in AAV manufacturing failures. Future research will concentrate on additional contributors to our truncation model to gain a deeper understanding of AAV manufacturing failures more generally.