Specification-driven machine learning (SDML) is an emerging approach that plays a pivotal role in ensuring that neural networks meet essential safety and robustness requirements. This methodology is particularly significant in domains where the consequences of system failures can be catastrophic, such as autonomous driving, healthcare, and aerospace. By integrating formal specifications into the machine learning