用参与式绘图揭示算法注册表在社会治理中的盲区与价值
Co-constructing sociotechnical AI governance: participatory system mapping using algorithm registers
- 通过多方参与绘制系统图谱,挖掘算法注册表未披露的治理细节
- 发现注册表遗漏了福利资格拒批、系统性能下降等关键风险
- 适合关注算法透明化与公共治理的政策制定者与社会工作者
算法注册表被倡导为提升公共服务中算法使用透明度的手段。然而,不同公众对透明内容与方式的期待各异,且现有注册表难以呈现算法嵌入的社会技术系统全貌,也未能有效促进问责。本文以荷兰某城市市政算法注册表为案例,研究基于业务规则引擎实现法律自动化的福利资格评估决策支持工具。通过访谈、问卷及参与式系统映射工作坊(参与者包括市政职员、民间组织与监察员,共8人),探究注册表在多大程度上帮助利益相关方描绘该算法系统的运行图景。这些图谱用于构建系统理论过程分析(STPA),将注册表置于更广泛的社会技术治理结构中。参与者贡献识别出仅靠注册表无法发现的安全隐患,包括福利资格被错误拒绝、系统性能恶化以及无法申诉不当决定。通过整合直接与间接利益相关者视角,反思算法治理中的规范性维度及其如何受政治影响。
原文摘要 · Abstract (English)
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.
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