An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services
This addresses the problem of trustworthiness in cloud AI services for developers and users, but it is incremental as it builds on existing XAI methods.
The paper tackles the opacity of cloud AI services by designing an open-API-based explainable AI (XAI) service to provide feature contribution explanations, with results showing the architecture is cloud-agnostic and data augmentations improve XAI consistency metrics.
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.