Here, machine learning (ML) for anomaly generation can reveal possible failure modes in data. By extension, generative models can enable constructing and exploring an "anomalous data space" that reveals previously unknown or non-intuitive but actionable anomaly criteria. Similarly, generative exploration can reveal failure modes in data representations as unexpected behavior that deviates from the norm in functionality. Such "representational anomalies" do not indicate semantic anomalies but expose representation definition or syntax loopholes. Representational anomalies are specific to their data.