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research-article

XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development

Authors:

Abishek Arumugam Thiruselvi,

Abdelwahab Hamou-LhadjAuthors Info & Claims

ICSE-NIER'24: Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results

Pages 67 - 71

https://doi.org/10.1145/3639476.3639759

Published: 24 May 2024 Publication History

Abstract

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.

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Cited By

Wang ZLiu YHuang J(2024)An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI ServicesIEEE Transactions on Cloud Computing10.1109/TCC.2024.339860912:2(762-776)Online publication date: Apr-2024
https://doi.org/10.1109/TCC.2024.3398609
Wang ZLiu Y(2024)Cloud-Based XAI Services for Assessing Open Repository Models Under Adversarial Attacks2024 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE62657.2024.00031(141-152)Online publication date: 7-Jul-2024
https://doi.org/10.1109/SSE62657.2024.00031

Index Terms

XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
1. Applied computing
  1. Operations research
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Published In

cover image ACM Conferences

ICSE-NIER'24: Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results

April 2024

127 pages

ISBN:9798400705007

DOI:10.1145/3639476

Co-chairs:
Ana Paiva,
Rui Abreu,
Robert Hierons
University of Sheffield, United Kingdom
,
Henrique Madeira
University of Coimbra Portugal
,
Program Co-chairs:
Abhik Roychoudhury,
Margaret Storey

Copyright © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Faculty of Engineering of University of Porto

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Published: 24 May 2024

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ICSE-NIER'24: 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results

April 14 - 20, 2024

Lisbon, Portugal

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Cited By

Wang ZLiu YHuang J(2024)An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI ServicesIEEE Transactions on Cloud Computing10.1109/TCC.2024.339860912:2(762-776)Online publication date: Apr-2024
https://doi.org/10.1109/TCC.2024.3398609
Wang ZLiu Y(2024)Cloud-Based XAI Services for Assessing Open Repository Models Under Adversarial Attacks2024 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE62657.2024.00031(141-152)Online publication date: 7-Jul-2024
https://doi.org/10.1109/SSE62657.2024.00031

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