As the artificial intelligence (AI) supply chain grows more complex, AI
systems and models are increasingly likely to incorporate externally-sourced
ingredients such as datasets and other models. In such cases, determining
whether or not an AI system or model complies with the EU AI Act will require
gathering compliance-related metadata about both the AI system or model
at-large as well as those externally-supplied ingredients. There must then be
an analysis that looks across all of this metadata to render a prediction about
the compliance of the overall AI system or model. Up until now, this process
has not been automated. Thus, it has not been possible to make real-time
compliance determinations in scenarios where doing so would be advantageous,
such as the iterative workflows of today's AI developers, search and
acquisition of AI ingredients on communities like Hugging Face, federated and
continuous learning, and more. To address this shortcoming, we introduce a
highly automated system for AI Act compliance analysis. This system has two key
elements. First is an interlocking set of computational artifacts that capture
compliance-related metadata about both: (1) the AI system or model at-large;
(2) any constituent ingredients such as datasets and models. Second is an
automated analysis algorithm that operates across those computational artifacts
to render a run-time prediction about whether or not the overall AI system or
model complies with the AI Act. Working together, these elements promise to
enhance and accelerate AI Act compliance assessments.

通过引入一种高度自动化的系统，我们可以捕获与人工智能系统和模型的合规性相关的数据，并跨越这些数据进行分析，从而加强和加速 AI 法规的合规评估。

遵规卡：用于自动化 AI 监管遵守的计算工具

Compliance Cards: Computational Artifacts for Automated AI Regulation  Compliance

In this paper, we examine the work of data annotation. Specifically, we focus
on the role of counting or quantification in organising annotation work. Based
on an ethnographic study of data annotation in two outsourcing centres in
India, we observe that counting practices and its associated logics are an
integral part of day-to-day annotation activities. In particular, we call
attention to the presumption of total countability observed in annotation - the
notion that everything, from tasks, datasets and deliverables, to workers, work
time, quality and performance, can be managed by applying the logics of
counting. To examine this, we draw on sociological and socio-technical
scholarship on quantification and develop the lens of a 'regime of counting'
that makes explicit the specific counts, practices, actors and structures that
underpin the pervasive counting in annotation. We find that within the AI
supply chain and data work, counting regimes aid the assertion of authority by
the AI clients (also called requesters) over annotation processes, constituting
them as reductive, standardised, and homogenous. We illustrate how this has
implications for i) how annotation work and workers get valued, ii) the role
human discretion plays in annotation, and iii) broader efforts to introduce
accountable and more just practices in AI. Through these implications, we
illustrate the limits of operating within the logic of total countability.
Instead, we argue for a view of counting as partial - located in distinct
geographies, shaped by specific interests and accountable in only limited ways.
This, we propose, sets the stage for a fundamentally different orientation to
counting and what counts in data annotation.

研究数据注释的工作，特别关注计数或量化在组织注释工作中的作用。基于对印度两个外包中心的数据注释的民族志研究，观察到计数实践及其关联逻辑是日常注释活动的一个重要组成部分。提出计数制度的镜头，明确支持注释广泛的计数所依赖的特定计数、实践、参与者和结构。发现在人工智能供应链和数据工作中，计数制度有助于人工智能客户（也称为请求者）对注释过程的权威主张，使其变得简化、标准化和同质化。通过这些影响，说明了在全面可计算的逻辑下操作的局限性，并提出将计数视为局部化、由具体利益塑造且仅在有限程度上可追溯的观点。据此，提出了对计数和数据注释的基本不同取向的舞台设定。