XAIpipeline: Automated Orchestration of Explainable AI Services for Cloud AI and Open-source Models
Bibliographic record
Abstract
Cloud platforms and open-source model repositories offer advanced AI services. These services are becoming essential components for building AI -enabled software. The opacity and lack of explanation have become new challenges to address within the software service lifecycle. Recent studies demonstrate that augmenting explainability requires the integration of diverse al-gorithms, models, and data pipelines. To address this, we present XAIpipeline, a service that interfaces with cloud AI services and open-source models to provide detailed model explanations. XAIpipeline automates structured approaches to apply multiple explainable AI (XAI) techniques, enhancing the explainability and quality assurance of AI-based software service systems. This work implements the XAIpipeline's design and technology stack, demonstrating its integration of XAI algorithms, cloud AI services, and open-source models with a DevOps-style toolkit. The service executes parallel pipelines and produces end-to-end explanation visualizations from data samples. XAIpipeline offers APIs, CLIs, and web portals, enabling users to configure tasks to their specific requirements. We showcase three XAI service scenarios where AI models are applied to support decision-making: (1) Tabular classification model, (2) Image vision model, and (3) Video action recognition model. The source code and supplementary materials are available on GitHub (https://ithub.com/ZeruiWIXAlpipeline).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".