Problem
Research questions and friction points this paper is trying to address.
Feature Attribution
Model Interpretability
Software Fragmentation
Accessibility
LumiXAI: A Modular Full-Stack Framework for Feature Attribution
🤖 AI Summary
LumiXAI解决了解释模型时软件碎片化问题，通过提供一个模块化的全栈框架，整合了特征归因分析，并支持多种用户访问。
📝 Abstract
Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools specialize along narrow axes, such as a single modality, a code API or a GUI, or a fixed rather than extensible method set, and rarely combine these strengths. Moreover, many explainability tools are designed primarily for domain experts, requiring programming skills or familiarity with attribution methods that can make them difficult for non-expert users to access. In this article, we present LumiXAI, a modular full-stack framework that consolidates attribution analysis into a single system. It couples classification and generative attribution with an interactive GUI supporting bidirectional exploration, a plug-in architecture for registering new models and methods, and three access tiers serving non-programmers, developers, and extenders from one backend. Its contribution is a system that operationalises established attribution methods under one interface, one interaction model, and one persistence layer, with containerised services and persistent results making analyses reproducible across machines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
modular full-stack framework
feature attribution
interactive GUI
plug-in architecture
reproducibility
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