Volume 11 • Issue 2 • PP: 50–59 • 2026
Designing Algorithmic Accountability for Citizens: Developing and Validating a Three-Layer Transparency Framework for Public Sector Decision Systems Through Iterative Participatory Prototype Design
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When governments use algorithmic systems to determine eligibility for housing support, welfare benefits, or social services, the citizens whose lives are most directly affected are often the least equipped to understand, scrutinise, or challenge the outcomes. Standard decision notices provide statutory reference numbers and outcome statements without any meaningful account of which data was used, why the algorithm produced the result it did, or what a citizen can realistically do next. This accountability gap is not merely a design inconvenience; it erodes the procedural fairness that democratic governance requires, and it disproportionately affects the most vulnerable service users. This paper reports a three-phase research programme in which a principled transparency framework for citizen-facing algorithmic decision interfaces was developed and validated through sustained engagement with end users. A needs assessment with 142 citizens and 18 civil servant interviews established what transparency citizens actually require. Three iterative co-design workshops with 24 citizens and 8 frontline officials produced progressively refined interface prototypes organised around three distinct transparency layers—process disclosure, rationale explanation, and contestation support. A subsequent think-aloud evaluation with 36 citizens compared four interface conditions ranging from the current opaque standard to the full three-layer framework. The fully layered interface substantially outperformed the existing standard and all partial implementations across trust, perceived actionability, comprehension, and transparency satisfaction. The paper contributes the framework itself as a theoretically grounded and empirically validated design resource, a set of evidence-based design guidelines derived from across all three study phases, and a replicable participatory methodology for involving affected citizens in the design of AI governance interfaces.
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