Explanations of AI systems rarely address the information needs of people affected by algorithmic decision-making (ADM). This gap between conveyed information and information that matters to affected stakeholders can impede understanding and adherence to regulatory frameworks such as the AI Act. To address this gap, we present the "XAI Novice Question Bank": A catalog of affected stakeholders' information needs in two ADM use cases (employment prediction and health monitoring), covering the categories data, system context, system usage, and system specifications. Information needs were gathered in an interview study where participants received explanations in response to their inquiries. Participants further reported their understanding and decision confidence, showing that while confidence tended to increase after receiving explanations, participants also met understanding challenges, such as being unable to tell why their understanding felt incomplete. Explanations further influenced participants' perceptions of the systems' risks and benefits, which they confirmed or changed depending on the use case. When risks were perceived as high, participants expressed particular interest in explanations about intention, such as why and to what end a system was put in place. With this work, we aim to support the inclusion of affected stakeholders into explainability by contributing an overview of information and challenges relevant to them when deciding on the adoption of ADM systems. We close by summarizing our findings in a list of six key implications that inform the design of future explanations for affected stakeholder audiences.

翻译：人工智能系统的解释很少能有效满足受算法决策（ADM）影响人群的信息需求。这种传递信息与利益相关者关切信息之间的差距，会阻碍人们对《人工智能法案》等监管框架的理解和遵守。为弥补这一差距，我们提出"XAI新手问题库"：这是一份针对两种ADM应用场景（就业预测与健康监测）中受影响利益相关者信息需求的分类目录，涵盖数据、系统环境、系统使用和系统规范四个类别。信息需求通过访谈研究收集，参与者在提问后获得相应解释。参与者还报告了他们的理解程度与决策信心，结果显示：虽然接收解释后信心普遍提升，但参与者仍面临理解困难，例如无法明确为何对信息理解不够完整。解释还影响了参与者对系统风险与收益的认知，他们会根据具体场景确认或改变原有看法。当感知风险较高时，参与者对意图性解释（如系统设立的原因与目的）表现出特别兴趣。本研究旨在通过梳理利益相关者在决策是否采纳ADM系统时关注的信息与面临的挑战，促进将受影响群体纳入可解释性范畴。最后，我们总结出六项关键启示，为面向受影响利益相关者群体的未来解释设计提供指导。