Recording model and data provenance creates quantifiable functional requirements whose verification confirms ML interpretability.
Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Munich, Germany: Self-published
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Verifying Machine Learning Interpretability Requirements through Provenance
Recording model and data provenance creates quantifiable functional requirements whose verification confirms ML interpretability.