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arxiv: 2604.21599 · v1 · submitted 2026-04-23 · 💻 cs.SE · cs.LG

Verifying Machine Learning Interpretability Requirements through Provenance

Lynn Vonderhaar , Juan Couder , Daryela Cisneros , Omar Ochoa This is my paper

Pith reviewed 2026-05-09 20:50 UTC · model grok-4.3

classification 💻 cs.SE cs.LG
keywords machine learninginterpretabilityprovenancerequirements engineeringnon-functional requirementsfunctional requirementsML engineering
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The pith

Saving model and data provenance during machine learning development creates measurable functional requirements that verify interpretability.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper contends that interpretability, treated as a non-functional requirement in machine learning, can be verified by recording provenance information about models and data. This recording turns opaque model behavior into a set of specific, checkable functional requirements whose satisfaction confirms the broader interpretability goal. A sympathetic reader would care because it supplies a concrete verification path for a quality that existing literature leaves unmeasurable. The approach imports ideas from requirements engineering to increase rigor in machine learning engineering.

Core claim

The paper claims that saving various types of model and data provenance makes the model's behavior transparent and interpretable. This data forms the basis of quantifiable functional requirements whose verification in turn verifies the interpretability non-functional requirement.

What carries the argument

ML provenance, consisting of saved records of model and data details, serves as the central mechanism that renders behavior transparent and supports the creation of verifiable functional requirements.

If this is right

  • Engineers obtain a practical method to verify interpretability non-functional requirements for machine learning models.
  • Quantifiable functional requirements derived from provenance become the operational checks that stand in for the abstract interpretability goal.
  • Machine learning development gains a verification technique drawn from requirements engineering.
  • Transparency of model behavior increases when provenance is systematically saved.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same provenance approach might apply to verifying other machine learning non-functional requirements such as fairness or robustness.
  • Embedding provenance capture into standard machine learning pipelines could narrow the gap between machine learning practice and traditional software engineering.
  • Empirical tests in deployed systems could show whether verified functional requirements actually improve human understanding of model outputs.

Load-bearing premise

The premise that recording provenance data will make model behavior transparent and interpretable enough for functional-requirement checks to confirm the non-functional interpretability requirement.

What would settle it

An ML model in which all relevant provenance is recorded and the derived functional requirements are verified, yet experts or users still cannot interpret the model's decisions.

Figures

Figures reproduced from arXiv: 2604.21599 by Daryela Cisneros, Juan Couder, Lynn Vonderhaar, Omar Ochoa.

Figure 1
Figure 1. Figure 1: The three “Starting Point” classes and some of their subclasses in PROV [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example Python code for saving provenance data. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Section of the linear regression provenance graph [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Model parameters saved to the provenance graph [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that ML provenance (including model and data lineage, hyperparameters, and training logs) can be saved to make model behavior transparent and interpretable. This provenance data underpins quantifiable Functional Requirements (FRs) whose verification directly confirms the Non-Functional Requirement (NFR) of interpretability, which existing literature treats as immeasurable. The work contributes a high-level approach for ML engineers to operationalize interpretability verification via requirements engineering techniques.

Significance. If the proposed mapping from provenance-derived FRs to interpretability NFR holds and is validated, the paper would provide a practical bridge between software requirements engineering and ML development, enabling auditable and verifiable interpretability in ML pipelines. This addresses a recognized gap in making ML NFRs rigorous without relying solely on post-hoc explanation techniques.

major comments (2)
  1. [Abstract] Abstract: The assertion that 'Saving this data forms the basis of quantifiable Functional Requirements (FRs) whose verification in turn verifies the interpretability NFR' lacks any concrete example, logical derivation, or reduction showing how FR satisfaction (e.g., confirming training data source or model version) entails satisfaction of interpretability properties such as feature contributions or local decision explanations. This entailment is load-bearing for the central claim.
  2. The manuscript presents only a conceptual framework with no case study, formal model, or validation steps demonstrating that provenance records address prediction-level interpretability questions rather than solely enabling reproducibility and traceability.
minor comments (2)
  1. [Abstract] The abstract would benefit from a brief inline definition or citation for 'ML provenance' and 'quantifiable FRs' to improve accessibility for readers unfamiliar with the intersection of requirements engineering and ML.
  2. Consider including a diagram or table that explicitly links specific provenance types (lineage, hyperparameters, logs) to example FRs and the interpretability aspects they purportedly verify.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify that the central claim requires clearer support. We address each point below and outline planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that 'Saving this data forms the basis of quantifiable Functional Requirements (FRs) whose verification in turn verifies the interpretability NFR' lacks any concrete example, logical derivation, or reduction showing how FR satisfaction (e.g., confirming training data source or model version) entails satisfaction of interpretability properties such as feature contributions or local decision explanations. This entailment is load-bearing for the central claim.

    Authors: We acknowledge that the abstract states the entailment at a high level without an explicit example or derivation. The manuscript defines interpretability via transparency and traceability (Section 2), arguing that provenance-derived FRs (e.g., 'training data source and version are recorded and match the deployed model') provide the necessary context for any downstream interpretability analysis, including feature contributions. However, we agree a concrete illustration is missing. In revision we will expand the abstract and add a short example in the introduction showing how verification of a data-lineage FR enables assessment of whether a local explanation (such as LIME) is based on the intended training distribution. revision: yes

  2. Referee: The manuscript presents only a conceptual framework with no case study, formal model, or validation steps demonstrating that provenance records address prediction-level interpretability questions rather than solely enabling reproducibility and traceability.

    Authors: The paper's stated contribution is a conceptual mapping from provenance to verifiable FRs that operationalize the interpretability NFR; it does not include empirical validation or a formal model. We maintain that provenance supports prediction-level questions by supplying the exact data and model context required for local explanations, but we accept that the current text does not demonstrate this link beyond reproducibility. We will add an illustrative scenario (not a full case study) in a new subsection showing how logged prediction-specific provenance can be used to verify that a local explanation was generated from the correct input slice. revision: partial

Circularity Check

0 steps flagged

No circularity: conceptual proposal without derivations or self-referential reductions

full rationale

The paper is a requirements-engineering proposal that links provenance records to FRs whose verification is asserted to confirm the interpretability NFR. No equations, parameters, derivations, or formal reductions appear in the abstract or described content. The central mapping is presented as a methodological contribution rather than a result derived from prior quantities or self-citations. No load-bearing self-citation chains, ansatzes, or renamings of known results are exhibited. The work is therefore self-contained as a high-level suggestion and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that provenance records can be turned into verifiable FRs that confirm interpretability, without independent evidence or justification supplied.

axioms (1)
  • domain assumption Provenance data can be saved to make model behavior transparent and interpretable
    Invoked as the basis for turning NFR into FRs but not derived or evidenced in the abstract.

pith-pipeline@v0.9.0 · 5482 in / 1056 out tokens · 41110 ms · 2026-05-09T20:50:26.181022+00:00 · methodology

discussion (0)

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Reference graph

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