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

arXiv: https://arxiv.org/abs/2403.16858 · HTML: https://mlsanity.com/paper/2403.16858/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup](https://mlsanity.com/paper/2009.06962/) (2020)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs](https://mlsanity.com/paper/2008.02312/) (2020)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology](https://mlsanity.com/paper/2003.05155/) (2020)
- [Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development](https://mlsanity.com/paper/2110.02932/) (2021)
- [Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest](https://mlsanity.com/paper/2203.11063/) (2022)

## Built on by (most cited first, 3 of 3)

- [STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video Models](https://mlsanity.com/paper/2411.00630/) (2024)
- [Towards Understanding the Optimization Mechanisms in Deep Learning](https://mlsanity.com/paper/2503.23016/) (2025)
- [Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AI](https://mlsanity.com/paper/2605.11687/) (2026)

## Abstract (arXiv)

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.