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.

翻译：可解释人工智能旨在通过统一解决方案使复杂算法对用户透明化。研究者强调，整合领域特定情境对于开发面向最终用户的解释至关重要。本研究聚焦于德国的Schufa住房评分系统，探究用户的信息需求及对解释的期望如何因其角色而异。通过推测性设计方法，我们邀请商务信息专业学生设想分别从租客和房东视角提供住房信用评分解释的用户界面。初步研究结果表明，尽管存在适用于所有用户的一般性需求，但亦有因角色实践现实及信用评分影响方式不同而产生的冲突性需求。我们通过提出未来研究方向——考察用户解释需求时需考虑其角色与能动性——为以人为中心的XAI研究做出贡献。