审计中的语境与对称性：以动作捕捉中的骨架推理为例
Context and Symmetry in Auditing: A Case Study of Skeleton Inference in Motion Capture
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中文总结 AI 辅助
本文以动作捕捉的骨架推理为案例，提出语境审计方法，结合对称性概念，用于评估AI系统行为与应然表现的契合度，为AI审计提供新思路。
中文摘要 AI 辅助
人们越来越需要与能够观察并对人类做出推断的AI系统交互，但这些系统是否真的有效？回答这个问题的标准方法是AI审计。开展AI审计需要确定系统的行为方式（即确定用何种类型的输入对其进行审计，然后观察并记录系统的实际行为），并将其与系统应有的行为方式（即确定系统的标称输出应是什么样）进行对比。我们认为，这最好通过语境审计来完成，我们将其作为一种在产生测量结果的实践语境中对测量进行审计的方法引入。我们展示了语境审计如何能够质询审计过程中隐含的假设，并让审计人员明确何为基准真值，我们将其定义为用于评估系统的、关于现实世界的可验证测量结果。我们概述了来自科学技术研究的对称性概念如何在基准真值未知、不可知或有争议时支持审计。最后，为了展示语境审计和对称审计在实践中如何开展，我们呈现了动作捕捉中骨架推理的案例研究，并提出我们的案例研究表明的、对动作捕捉系统未来审计特别有成效的领域。
英文摘要
Humans are increasingly expected to interact with AI systems that observe and make inferences about them - but do these systems actually work? A standard approach to answering this question is AI auditing. Conducting an AI audit requires identifying how a system behaves (i.e., determining what types of inputs to audit it with and then observing and documenting actual system behavior) and contrasting that with how a system should behave (i.e., determining what the nominal outputs of a system should look like). We argue that this is best done through a contextual audit, which we introduce as a method for auditing measurements within the context of the practices that produce them. We show how contextual auditing enables the interrogation of assumptions implicit in the audit process and allows auditors to be explicit about what serves as ground truth, which we define as verifiable measurements about the real world against which systems are evaluated. We outline how the concept of symmetry from science and technology studies can enable audits when ground truth is unknown, unknowable, or contested. Finally, to demonstrate how contextual and symmetric audits can be conducted in practice, we present a case study of skeleton inference in motion capture and propose areas suggested by our case study as particularly fruitful for future audits of motion capture systems.
