Verifying Machine Learning Interpretability Requirements through Provenance
Researchers propose using ML provenance tracking to make interpretability requirements measurable and verifiable in ML engineering. The work bridges requirements engineering and ML development by addressing a critical gap: interpretability has been defined qualitatively but never rigorously validated.
Modelwire context
ExplainerThe paper's core contribution isn't a new interpretability method but a meta-level claim: that interpretability requirements in ML systems have never been formally verifiable, meaning teams have been shipping 'interpretable' models without any agreed mechanism to confirm that claim holds. Provenance tracking is proposed as the audit trail that makes verification possible.
This sits in productive tension with recent coverage of interpretability methods on Modelwire. The ORCA paper from April 16 introduced a post-training framework for SVMs that quantifies feature contributions without retraining, which is exactly the kind of method this paper would need to evaluate against a formal requirements spec. ORCA tells you what a model is doing; the provenance work asks whether that explanation actually satisfies a stated requirement. The two pieces address adjacent problems but neither cites the other's concern. More broadly, the InsightFinder funding story from the same week frames systemic observability as a commercial priority, and this research suggests the academic side is converging on similar ground from a requirements-engineering angle.
Watch whether any ML governance frameworks, such as those emerging from EU AI Act compliance tooling, adopt provenance-based interpretability verification as a required audit artifact within the next 12 to 18 months. Adoption there would validate the approach far faster than academic citation counts.
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MentionsMachine Learning Engineering · ML provenance · interpretability requirements
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