The growing use of AI in public administration improves efficiency, yet its use in discretionary decisions raises concerns about fairness and legitimacy. While prior research examined decision stakes and Human–AI decision-making configurations separately, their combined effect on citizens’ perceptions of fairness and adoption remains underexplored. We conducted a mixed-method Wizard-of-Oz study (n=43) using an Intelligent-Self-Service-Kiosk. Participants completed a low-stakes (ID-renewal) and a high-stakes (social-housing) task under one of three decision-making configurations: AI alone, AI with human-supervision, and human with AI advice or recommendation. Quantitative analysis found no significant effects, highlighting the limits of standard metrics. However, qualitative interviews revealed that citizens valued human involvement, requiring meaningful over symbolic oversight. They emphasized interactive dialogue before decisions to capture their circumstances and after, to facilitate appeals. We contribute evidence of tensions between citizens’ desire for efficiency and need for human-control and fairness. We provide guidance for designing citizen-centered AI systems that align with democratic values.

In this paper, banking services, as the research object to explore the relationship among distributive fairness, procedural fairness, interactive fairness and informational fairness. The study found: informational fairness significant impact on the ...

In this paper, we introduce an innovative design concept for the curation of data, which we call 'Data Story'. We view this as an additional resource for data curation, aimed specifically at supporting the sharing of qualitative and ethnographic data. The Data Story concept is motivated by three elements: 1. the increased attention of funding agencies and academic institutions on Research Data Management and Open Science; 2. our own work with colleagues applying ethnographic research methods; and 3. existing literature that has identified specific challenges in this context. Ongoing issues entailed in dealing with certain contextual factors that are inherent to qualitative research reveal the extent to which we still lack technical design solutions that can support meaningful curation and sharing. Data Story provides a singular way of addressing these issues by integrating traditional data curation approaches, where research data are treated as 'objects' to be curated and preserved according to specific standards, with a more contextual, culturally-nuanced and collaborative organizing layer that can be thought of as a "Story". The concept draws on existing literature on data curation, digital storytelling and Critical Data Studies (CDS). As a possible design solution for Research Data Management and data curation, Data Story offers: 1) a collaborative workflow for data curation; 2) a story-like format that can serve as an organizing principle; 3) a means of enhancing and naturalizing curation practices through storytelling. Data Story is currently being developed for deployment and evaluation.