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关注受算法决策影响人群的信息需求。这种传递信息与利益相关者关注信息之间的差距，可能阻碍其对《人工智能法案》等监管框架的理解与遵守。为弥补这一差距，我们提出“XAI初学者问答库”：涵盖受算法影响利益相关者在两种ADM应用场景（就业预测与健康监测）中的信息需求目录，包括数据、系统环境、系统使用和系统规范等类别。通过访谈研究收集信息需求，参与者根据提问获得相应解释。参与者还报告了其理解程度与决策信心，结果显示，尽管接受解释后信心趋于增强，但参与者面临理解挑战，例如无法说明理解为何存在不完整感。解释进一步影响了参与者对系统风险与收益的认知，这些认知会根据具体应用场景得到确认或发生改变。当风险感知较高时，参与者对系统意图的解释（例如系统部署的原因与目的）表现出特殊兴趣。本研究旨在通过提供与受算法决策影响者在决定是否采用ADM系统时相关的信息与挑战概览，促进其参与可解释性。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。