Pre-demolition audits, central to sustainable urban material recovery, require decisions that are defensible - legible, well-sourced, and contestable - not just accurate. This paper argues that explainable AI and knowledge graphs each solve part of this problem and proposes four integration modes (Lifting, Constraining, Typing, Revising) explaining how combining them produces defensibility properties neither achieves alone, illustrated through a building-materials example using open building-data standards. Applications include supporting auditors and regulators in urban mining and demolition assessments, improving transparency and accountability in AI-assisted environmental compliance decisions, and informing broader design of explainable decision-support systems in regulated domains.
Authors: Jan Gronewald, Andreas Emrich, Nijat Mehdiyev
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