LumiXAI：一个用于特征归因的模块化全栈框架

中文摘要 AI 辅助

特征归因是模型可解释性的核心工具，但应用它的软件仍处于碎片化状态：单个工具仅专注于狭窄的维度，例如单一模态、代码API或图形用户界面（GUI），或是固定而非可扩展的方法集，很少能结合这些优势。此外，许多可解释性工具主要面向领域专家设计，需要编程技能或对归因方法的熟悉度，这使得非专业用户难以使用。本文提出LumiXAI，一个模块化全栈框架，将归因分析整合到单一系统中。它将分类与生成式归因相结合，支持双向探索的交互式GUI、用于注册新模型和方法的插件架构，以及从一个后端服务非程序员、开发者和扩展者的三个访问层级。其贡献是一个在单一接口、单一交互模型和单一持久层下实现成熟归因方法的系统，带有容器化服务和持久化结果，使分析可在不同机器间复现。

英文摘要

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.

发表机构

• Università degli Studi di Milano（米兰大学）

机构由 AI 辅助整理，请以论文原文为准。