Co-constructing sociotechnical AI governance: participatory system mapping using algorithm registers

2026-08-12

AI Summary

This study addresses the limitations of existing algorithmic registry frameworks, which often fail to adequately capture the embedded sociotechnical systems and are hampered by divergent stakeholder expectations and understandings of transparency, thereby undermining accountability. Integrating System-Theoretic Process Analysis (STPA), participatory systems mapping, interviews, and surveys, the research engages diverse stakeholders to co-construct a governance landscape for welfare eligibility assessment algorithms. By situating algorithmic registries within a broader sociotechnical governance framework, the project identifies critical risks—such as wrongful denials, system performance degradation, and breakdowns in appeal mechanisms—that registries alone cannot reveal. Furthermore, it uncovers overlooked normative and political dimensions of algorithmic governance, advocating for safety analyses that are more inclusive and politically attuned.

Abstract

Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics differ in their expectations of what should be made transparent and how, as well as in their interest in and ability to parse the information currently published in the registers. Moreover, it remains unclear how these instruments can represent the sociotechnical systems in which these algorithms are embedded, and how system-level transparency can facilitate accountability. In this paper, we ask, what do algorithm registers reveal (and occlude) about the sociotechnical systems governing algorithmic systems, and how can diverse stakeholder perspectives inform a more pluralistic system-theoretic safety analysis? To do this, we probe the municipal algorithm register of a Dutch city through a case study of a decision-support tool for caseworkers' assessment of citizens' welfare benefits eligibility based on legal automation through a business rule engine. Through interviews, surveys, and participatory system mapping workshops (with municipal staff, civil society organisations, and ombudsmen, N=8), we seek to understand to what extent the register allows stakeholders to map the algorithmic system in question. These maps inform a System-Theoretic Process Analysis (STPA) that situates the register within a wider sociotechnical governance structure. Participants' contributions allow us to identify potential safety hazards which would not have been possible to see using the algorithm register alone, including benefits eligibility denial, system performance deterioration, and inability to contest wrongful decisions. By engaging both direct and indirect stakeholders, we reflect on the normative dimensions of algorithm governance efforts and how politics shape the practice of system safety analysis.

Problem

• algorithm registers
• sociotechnical systems
• transparency
• accountability
• stakeholder perspectives

Innovation

• participatory system mapping
• algorithm registers
• sociotechnical governance
• System-Theoretic Process Analysis (STPA)
• algorithmic accountability