Designing Algorithmic Accountability for Citizens: Developing

and Validating a Three-Layer Transparency Framework for

Public Sector Decision Systems Through Iterative Participatory

Prototype Design

Arash Salehpour1,* Laula Zhumabayeva2

1 Department of Cybersecurity, University of Istanbul, Türkiye

2 Department of Computer Science, Yessenov University, Aktau City, Kazakhstan

Received: December 18, 2026 Revised: February 09, 2026 Accepted: March 20, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Algorithmic transparency Public sector AI Participatory design Explainable AI Citizen-centred design

Automated decision-making Government algorithms Accountability Human-computer interaction

1. INTRODUCTION

Algorithmic systems are now embedded in the administrative

machinery of public services across most developed nations.

They assess housing benefit eligibility, calculate council tax

reductions, assign welfare fraud risk scores, predict child

protection need, and rank jobseekers for employment sup-