  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.
