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LumiXAI addresses the fragmentation in model interpretability by offering a unified, modular framework that integrates classification and generative attribution methods into a single reproducible system. By supporting multiple access tiers—from non-programmers to developers—it lowers the barrier to entry for feature analysis while maintaining extensibility through its plug-in architecture. This consolidation of tools under one interface represents a significant step toward operationalizing established explainability techniques across diverse user groups.
LumiXAI: A Modular Full-Stack Framework for Feature Attribution
arXiv:2608.24524v1 Announce Type: cross 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.
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.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2608.24524 [cs.SE]
(or arXiv:2608.24524v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2608.24524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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