XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913282410938368 |
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| author | Wang, Zerui Liu, Yan Thiruselvi, Abishek Arumugam Hamou-Lhadj, Abdelwahab |
| author_facet | Wang, Zerui Liu, Yan Thiruselvi, Abishek Arumugam Hamou-Lhadj, Abdelwahab |
| contents | In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_16858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development Wang, Zerui Liu, Yan Thiruselvi, Abishek Arumugam Hamou-Lhadj, Abdelwahab Artificial Intelligence In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability. |
| title | XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.16858 |