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满足受算法决策（ADM）影响的用户信息需求。这种传递信息与利益相关者关注信息之间的鸿沟，可能阻碍用户对《人工智能法案》等监管框架的理解与遵循。为弥合这一鸿沟，我们提出"XAI新手问题库"：针对两种ADM应用场景（就业预测与健康监测）中利益相关者的信息需求分类目录，涵盖数据、系统背景、系统使用及系统规范四个类别。通过访谈研究收集信息需求，参与者在提出疑问后获得系统解释。参与者还报告了理解程度与决策信心，结果表明：尽管解释后信心普遍提升，但参与者仍面临理解挑战（例如无法判断认知不完整的原因）。解释进一步影响参与者对系统风险与收益的认知，且其观点会因具体应用场景而确认或改变。当感知到高风险时，参与者尤其关注系统意图类解释（如系统部署原因与目标）。本研究通过梳理利益相关者在采用ADM系统决策时关心的关键信息与挑战，致力于推动将受影响群体纳入可解释性范畴。最后，我们总结六项核心发现，为面向该群体的未来解释设计提供指导。