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Explainable Artificial Intelligence is a concept aimed at making complexalgorithms transparent to users through a uniform solution. Researchers havehighlighted the importance of integrating domain specific contexts to developexplanations tailored to end users. In this study, we focus on the Schufahousing scoring system in Germany and investigate how users information needsand expectations for explanations vary based on their roles. Using thespeculative design approach, we asked business information students to imagineuser interfaces that provide housing credit score explanations from theperspectives of both tenants and landlords. Our preliminary findings suggestthat although there are general needs that apply to all users, there are alsoconflicting needs that depend on the practical realities of their roles and howcredit scores affect them. We contribute to Human centered XAI research byproposing future research directions that examine users explanatory needsconsidering their roles and agencies.

Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System