Summary of Xaiport: a Service Framework For the Early Adoption Of Xai in Ai Model Development, by Zerui Wang et al.
XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
by Zerui Wang, Yan Liu, Abishek Arumugam Thiruselvi, Abdelwahab Hamou-Lhadj
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes the early adoption of Explainable AI (XAI) to ensure trustworthy explanations for AI models. The researchers focus on three key properties: Quality of explanation, Architectural Compatibility, and Configurable operations. To achieve this, they introduce XAIport, a framework that encapsulates XAI microservices into Open APIs, enabling configurable XAI operations alongside machine learning development. The study also compares the operational costs of incorporating XAI with traditional machine learning using three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. The results show comparable operational costs between XAI and traditional machine learning, with XAIport improving both cloud AI model performance and explanation stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary XAI is a new way to explain how artificial intelligence (AI) works. The idea is to make sure that AI models are transparent and trustworthy by providing clear explanations for their decisions. To do this, the researchers created a framework called XAIport that helps developers integrate XAI into their machine learning projects. They tested XAIport with three different cloud-based computer vision services and found that it didn’t add much extra cost or complexity to the process. |
Keywords
» Artificial intelligence » Machine learning
