发表机构
TU Delft（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文以荷兰某城市算法登记册为案例，通过多利益相关者参与式映射结合STPA分析，揭示登记册的遮蔽性，为多元社会技术型AI治理提供新视角。
AI 中文摘要
算法登记册被推崇为公共服务中算法使用提供透明度的手段。然而，不同潜在公众对应公开什么、如何公开的期望存在差异，对当前登记册中发布信息的兴趣和解析能力也各不相同。此外，这些工具如何体现算法嵌入的社会技术系统，以及系统层面的透明度如何促进问责制，仍不明确。本文中，我们提出问题：算法登记册揭示（及遮蔽）了算法系统治理的社会技术系统的哪些方面，不同利益相关者视角如何为更具多元性的系统理论安全分析提供信息？为此，我们以荷兰某城市的市政算法登记册为研究对象，针对一款基于业务规则引擎法律自动化、用于社工评估公民福利资格的决策支持工具展开案例研究。通过访谈、调查和参与式系统映射研讨会（参与者包括市政工作人员、民间社会组织和监察员，共8人），我们探究登记册在多大程度上允许利益相关者绘制相关算法系统的图谱。这些图谱为系统理论过程分析（STPA）提供了依据，将登记册置于更广泛的社会技术治理结构中。参与者的贡献使我们能够识别出仅依靠算法登记册无法发现的潜在安全隐患，包括福利资格被拒绝、系统性能恶化以及无法对错误决定提出异议。通过与直接和间接利益相关者互动，我们反思算法治理工作的规范维度，以及政治如何塑造系统安全分析的实践。
英文摘要
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.
