扩展机器学习抽象边界：整合社会背景的复杂系统方法
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context
摘要：
机器学习（ML）公平性研究往往主要集中在对通常不透明的算法或模型和/或其直接输入和输出的基于推理的干预上。这种过于简化的数学模型抽象了ML模型被构思、开发和最终部署的潜在社会背景。由于公平本身是一个社会构建的概念，源于社会背景以及模型输入和模型本身，因此缺乏对社会背景的深入理解很容易破坏对ML公平的追求。在本文中，我们概述了三个新的工具，以提高理解，识别和社会背景的代表。首先，我们提出了一个基于复杂自适应系统（CAS）的模型和社会背景的定义，这将有助于研究人员和产品开发人员扩展ML公平工作的抽象边界，以包括社会背景。其次，我们引入协作因果理论形成（CCTF）作为一个关键的能力，建立一个社会技术框架，采用不同的心理模型和相关的因果理论建模的问题和解决方案的ML为基础的产品空间。最后，我们将基于社区的系统动力学（CBSD）确定为在ML产品开发过程的所有阶段实践CCTF的强大，透明和严格的方法。最后，我们讨论了这些系统的理论方法，以了解社会背景下，社会技术系统嵌入可以提高公平和包容性ML为基础的产品的发展。
英文摘要：
Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs. Such oversimplified mathematical models abstract away the underlying societal context where ML models are conceived, developed, and ultimately deployed. As fairness itself is a socially constructed concept that originates from that societal context along with the model inputs and the models themselves, a lack of an in-depth understanding of societal context can easily undermine the pursuit of ML fairness. In this paper, we outline three new tools to improve the comprehension, identification and representation of societal context. First, we propose a complex adaptive systems (CAS) based model and definition of societal context that will help researchers and product developers to expand the abstraction boundary of ML fairness work to include societal context. Second, we introduce collaborative causal theory formation (CCTF) as a key capability for establishing a sociotechnical frame that incorporates diverse mental models and associated causal theories in modeling the problem and solution space for ML-based products. Finally, we identify community based system dynamics (CBSD) as a powerful, transparent and rigorous approach for practicing CCTF during all phases of the ML product development process. We conclude with a discussion of how these systems theoretic approaches to understand the societal context within which sociotechnical systems are embedded can improve the development of fair and inclusive ML-based products.
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