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：可解释人工智能是一种旨在通过统一解决方案使复杂算法对用户透明的概念。研究人员强调了整合特定领域背景以开发面向最终用户定制化解释的重要性。在本研究中，我们聚焦于德国的Schufa住房评分系统，探究用户的信息需求和解释期望如何因其角色而异。通过采用推测性设计方法，我们邀请商业信息专业的学生从租户和房东两种视角，构想能够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通用需求，但也存在取决于用户角色实践现实及其受信用评分影响方式的矛盾需求。我们通过提出未来研究方向（即考察用户基于自身角色和能动性的解释需求），为人本可解释人工智能研究做出了贡献。