Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System.
CoRR(2023)
摘要
Explainable Artificial Intelligence is a concept aimed at making complex
algorithms transparent to users through a uniform solution. Researchers have
highlighted the importance of integrating domain specific contexts to develop
explanations tailored to end users. In this study, we focus on the Schufa
housing scoring system in Germany and investigate how users information needs
and expectations for explanations vary based on their roles. Using the
speculative design approach, we asked business information students to imagine
user interfaces that provide housing credit score explanations from the
perspectives of both tenants and landlords. Our preliminary findings suggest
that although there are general needs that apply to all users, there are also
conflicting needs that depend on the practical realities of their roles and how
credit scores affect them. We contribute to Human centered XAI research by
proposing future research directions that examine users explanatory needs
considering their roles and agencies.
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