Researchers propose using ML provenance to verify interpretability requirements

This paper introduces a method for verifying machine learning interpretability requirements by leveraging ML provenance. The authors propose that by saving various types of model and data provenance, engineers can establish quantifiable functional requirements. Verifying these functional requirements then serves as a basis for confirming whether the model meets its interpretability NFR, addressing the challenge of interpretability being an immeasurable requirement. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Provides a novel approach to quantify and verify ML interpretability, potentially improving model transparency and trustworthiness.

RANK_REASON This is a research paper published on arXiv proposing a new method for verifying ML interpretability.

Read on arXiv cs.LG →

• arXiv
• Machine Learning

• paper
• safety