通过开放API架构，让云端AI服务解释其预测依据并评估可信度。
An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services
- 基于微服务设计，不暴露模型结构即可提供特征贡献解释。
- 实测证明该架构可跨云平台使用，且数据增强提升解释一致性。
- 适合关注AI可信性与可解释性的开发者和评估人员。
本文提出一种基于开放API的可解释AI（XAI）服务架构，为云端AI服务提供特征贡献解释。尽管云端AI服务广泛用于特定领域应用并具备精准学习指标，但其预测过程仍不透明。我们主张将XAI操作作为开放API集成到云端服务评估中。所提架构采用微服务设计，无需展开模型结构即可生成特征贡献解释，并能评估模型性能与XAI一致性指标，以衡量服务可信度。通过收集运行流水线中的溯源数据，实现XAI服务的可复现性。实验测试了主流云端视觉AI服务在模型性能与XAI一致性上的表现，结果表明该架构具有云无关性；同时，数据增强显著提升了XAI一致性指标。
原文摘要 · Abstract (English)
This article presents the design of an open-API-based explainable AI (XAI) service to provide feature contribution explanations for cloud AI services. Cloud AI services are widely used to develop domain-specific applications with precise learning metrics. However, the underlying cloud AI services remain opaque on how the model produces the prediction. We argue that XAI operations are accessible as open APIs to enable the consolidation of the XAI operations into the cloud AI services assessment. We propose a design using a microservice architecture that offers feature contribution explanations for cloud AI services without unfolding the network structure of the cloud models. We can also utilize this architecture to evaluate the model performance and XAI consistency metrics showing cloud AI services trustworthiness. We collect provenance data from operational pipelines to enable reproducibility within the XAI service. Furthermore, we present the discovery scenarios for the experimental tests regarding model performance and XAI consistency metrics for the leading cloud vision AI services. The results confirm that the architecture, based on open APIs, is cloud-agnostic. Additionally, data augmentations result in measurable improvements in XAI consistency metrics for cloud AI services.
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