Motion capture systems, used across various domains, make body
representations concrete through technical processes. We argue that the
measurement of bodies and the validation of measurements for motion capture
systems can be understood as social practices. By analyzing the findings of a
systematic literature review (N=278) through the lens of social practice
theory, we show how these practices, and their varying attention to errors,
become ingrained in motion capture design and innovation over time. Moreover,
we show how contemporary motion capture systems perpetuate assumptions about
human bodies and their movements. We suggest that social practices of
measurement and validation are ubiquitous in the development of data- and
sensor-driven systems more broadly, and provide this work as a basis for
investigating hidden design assumptions and their potential negative
consequences in human-computer interaction.

通过社会实践理论的视角，通过对一项系统文献综述研究（N=278）的发现进行分析，我们展示了测量和验证的社会实践是如何根深蒂固地融入运动捕捉设计和创新中的，并且我们展示了当代运动捕捉系统是如何延续对人体和其运动的假设。我们认为，在数据驱动和传感器驱动系统的开发中普遍存在测量和验证的社会实践，并以此为基础来调查隐藏设计假设及其在人机交互中的潜在负面后果。

机器中的尸体：动作捕捉技术中的测量和验证社会实践

The Cadaver in the Machine: The Social Practices of Measurement and  Validation in Motion Capture Technology

Artificial intelligence (AI) systems attempt to imitate human behavior. How
well they do this imitation is often used to assess their utility and to
attribute human-like (or artificial) intelligence to them. However, most work
on AI refers to and relies on human intelligence without accounting for the
fact that human behavior is inherently shaped by the cultural contexts they are
embedded in, the values and beliefs they hold, and the social practices they
follow. Additionally, since AI technologies are mostly conceived and developed
in just a handful of countries, they embed the cultural values and practices of
these countries. Similarly, the data that is used to train the models also
fails to equitably represent global cultural diversity. Problems therefore
arise when these technologies interact with globally diverse societies and
cultures, with different values and interpretive practices. In this position
paper, we describe a set of cultural dependencies and incongruencies in the
context of AI-based language and vision technologies, and reflect on the
possibilities of and potential strategies towards addressing these
incongruencies.

本文介绍了 AI (人工智能) 系统如何模拟人类行为以及 AI 技术与全球文化差异相交互时出现的问题，提出对语言和视觉技术中不同文化依赖性和不一致性的策略和可能性进行思考。

人工智能中的文化不协调

Cultural Incongruencies in Artificial Intelligence

Multi-agent models are a suitable starting point to model complex social
interactions. However, as the complexity of the systems increase, we argue that
novel modeling approaches are needed that can deal with inter-dependencies at
different levels of society, where many heterogeneous parties (software agents,
robots, humans) are interacting and reacting to each other. In this paper, we
present a formalization of a social framework for agents based on the concept
of Social Practices as high level specifications of normal (expected) behavior
in a given social context. We argue that social practices facilitate the
practical reasoning of agents in standard social interactions. Thus they can
support deliberations for complex situations just like conventions and norms.
However, they also come with a social context that gives handles for social
planning and deliberation in top of the normal functional deliberation. The
main goal of this paper is to give a formalization of social practices that can
be used as a basis for implementations and defining precise structures within
which social learning can take place.

本文介绍了一种基于社会实践作为给定社会环境下期望行为的高级规范的代理社会框架的正式化。论文旨在提供社会实践的正式化，以便用作实现的基础，并定义社会学习可以发生的精确结构。