记录保存本为透明问责,却可能反噬算法监管的自主性与隐私。
Accountability Capture: How Record-Keeping to Support AI Transparency and Accountability (Re)shapes Algorithmic Oversight
- 提出‘问责捕获’概念,揭示记录行为如何重构技术治理流程。
- 100名从业者调研显示,内部与外部问责存在普遍冲突,员工出现抵制现象。
- 警示记录制度可能加剧监控、侵犯隐私,需警惕其隐性代价。
问责制度通常推动记录保存,以支持透明度,实现监督、调查、争议和补救。然而,实施记录保存可能带来未被充分探讨的考量、风险与后果。本文通过引入并阐释‘问责捕获’——即记录保存引发的社会技术过程重构及其下游影响,分析算法系统如何被纳入问责框架。基于对100名从业者的调研,本文揭示了实践中的记录问题,并确认其与问责捕获的关联。研究还记录了广泛存在的记录实践,内部与外部问责要求间的张力,以及员工对问责捕获所强加做法的抵制。文章讨论这些效应在监控、隐私与数据保护方面的意义,提醒算法问责领域关注此类制度化记录的潜在负面影响。总体而言,本文表明,为支持算法透明度而推行的记录保存本身可能引发更广泛的影响,亟需从业者、研究者与政策制定者共同重视。
原文摘要 · Abstract (English)
Accountability regimes typically encourage record-keeping to enable the transparency that supports oversight, investigation, contestation, and redress. However, implementing such record-keeping can introduce considerations, risks, and consequences, which so far remain under-explored. This paper examines how record-keeping practices bring algorithmic systems within accountability regimes, providing a basis to observe and understand their effects. For this, we introduce, describe, and elaborate 'accountability capture' -- the re-configuration of socio-technical processes and the associated downstream effects relating to record-keeping for algorithmic accountability. Surveying 100 practitioners, we evidence and characterise record-keeping issues in practice, identifying their alignment with accountability capture. We further document widespread record-keeping practices, tensions between internal and external accountability requirements, and evidence of employee resistance to practices imposed through accountability capture. We discuss these and other effects for surveillance, privacy, and data protection, highlighting considerations for algorithmic accountability communities. In all, we show that implementing record-keeping to support transparency in algorithmic accountability regimes can itself bring wider implications -- an issue requiring greater attention from practitioners, researchers, and policymakers alike.
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