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An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services
  • Zerui Wang,
  • Yan Liu,
  • Jun Huang
Zerui Wang
Department of Electrical and Computer Engineering, Concordia University Montréal

Corresponding Author:[email protected]

Author Profile
Yan Liu
Department of Electrical and Computer Engineering, Concordia University Montréal
Jun Huang
Department of Electrical and Computer Engineering, Concordia University Montréal

Abstract

This paper presents the design of an open-API-based explainable AI (XAI) service to provide feature-contribution explanations for cloud AI services. Cloud AI services have broad usage in developing domain-specific applications with learning precision metrics. However, the underlying AI models 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 AI services. We can also utilize this architecture to evaluate the performance and XAI consistency metrics showing cloud AI services' trustworthiness. We collect provenance data from XAI operations to enable traceability within the XAI service. Furthermore, we present the discovery scenarios for the experimental tests regarding performance and XAI consistency metrics for the leading cloud AI services for computer vision. The results confirm the open-API-based architecture cloud-agnostic. Additionally, data augmentation has a marked improvement in XAI consistency metrics for the cloud AI services.
20 Dec 2023Submitted to TechRxiv
22 Dec 2023Published in TechRxiv