Data-Driven Discovery of Design Specifications (Student Abstract)

Ensuring a machine learning model’s trustworthiness is crucial to prevent potential harm. One way to foster trust is through the formal verification of the model’s adherence to essential design requirements. However, this approach relies on well-defined, application-domain-centric criteria with which to test the model, and such specifications may be cumbersome to collect in practice. We propose a data-driven approach for creating specifications to evaluate a trained model effectively. Implementing this framework allows us to prove that the model will exhibit safe behavior while minimizing the false-positive prediction rate. This strategy enhances predictive accuracy and safety, providing deeper insight into the model’s strengths and weaknesses, and promotes trust through a systematic approach.