🤖 AI Summary
This study addresses the longstanding challenge of treating machine learning interpretability as a non-functional requirement lacking quantifiable metrics and validation mechanisms. To bridge this gap, the work proposes an innovative approach that reframes interpretability as a verifiable functional requirement through the integration of data and model provenance. By synergizing principles from requirements engineering and machine learning engineering, the authors develop a systematic and operational verification framework. This framework enables, for the first time, the explicit specification and empirical validation of interpretability requirements, thereby substantially enhancing the engineering rigor and trustworthiness of machine learning system development.
📝 Abstract
Machine Learning (ML) Engineering is a growing field that necessitates an increase in the rigor of ML development. It draws many ideas from software engineering and more specifically, from requirements engineering. Existing literature on ML Engineering defines quality models and Non-Functional Requirements (NFRs) specific to ML, in particular interpretability being one such NFR. However, a major challenge occurs in verifying ML NFRs, including interpretability. Although existing literature defines interpretability in terms of ML, it remains an immeasurable requirement, making it impossible to definitively confirm whether a model meets its interpretability requirement. This paper shows how ML provenance can be used to verify ML interpretability requirements. This work provides an approach for how ML engineers can save various types of model and data provenance to make the model's behavior transparent and interpretable. Saving this data forms the basis of quantifiable Functional Requirements (FRs) whose verification in turn verifies the interpretability NFR. Ultimately, this paper contributes a method to verify interpretability NFRs for ML models.