A New Perspective on XAI: Scientific Theory Building for Auditable Artefacts

An explanation is an answer to a specific question. During the lifecycle of a black-box model, different stakeholders ask different questions, which current practice in explainable AI (XAI) will typically answer using isolated methods. What is missing is a principled way to consolidate explanatory information about a fixed black-box model over time to address emerging governance obligations and the diverse explanatory needs that arise across the lifecycle of a model. This position paper argues for the creation of an explanatory artefact that accompanies a black-box throughout its lifecycle and serves as a persistent, adaptable, and auditable basis for diverse stakeholder needs. The concept of a scientific theory offers a natural model for such an artefact. We adopt Constructive Empiricism (CE) as the philosophical grounding and translate its core values -- empirical adequacy, acceptance as commitment, and pragmatic virtues -- into concrete requirements for XAI. Explanations for concrete stakeholder needs are then obtained by querying the same maintained explanatory information through interfaces, rather than by producing isolated method outputs. We argue that SToBBs offer a principled foundation for lifecycle-scale XAI, and invite the community to develop, challenge, and refine this conceptualisation.