用模型溯源数据验证可解释性要求是否达标
Verifying Machine Learning Interpretability Requirements through Provenance
- 通过保存模型与数据的溯源信息，使行为可追溯
- 将可解释性转化为可验证的量化功能需求
- 适合关注模型可信度的ML工程团队
机器学习工程日益重要，需提升开发严谨性。该领域借鉴软件工程，特别是需求工程，定义了针对机器学习的非功能性需求（NFRs），其中可解释性是关键之一。然而，现有方法难以验证这类需求，尤其是可解释性，因其缺乏可测量性，无法确认模型是否满足要求。本文提出利用机器学习溯源技术，通过保存模型和数据的多种溯源信息，使模型行为透明可查，进而构建可量化的功能需求，其验证过程即等价于对可解释性非功能性需求的验证。本研究贡献了一种验证机器学习可解释性需求的方法。
原文摘要 · Abstract (English)
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.
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