Innovations in AI have focused primarily on the questions of "what" and
"how"-algorithms for finding patterns in web searches, for instance-without
adequate attention to the possible harms (such as privacy, bias, or
manipulation) and without adequate consideration of the societal context in
which these systems operate. In part, this is driven by incentives and forces
in the tech industry, where a more product-driven focus tends to drown out
broader reflective concerns about potential harms and misframings. But this
focus on what and how is largely a reflection of the engineering and
mathematics-focused training in computer science, which emphasizes the building
of tools and development of computational concepts.
As a result of this tight technical focus, and the rapid, worldwide explosion
in its use, AI has come with a storm of unanticipated socio-technical problems,
ranging from algorithms that act in racially or gender-biased ways, get caught
in feedback loops that perpetuate inequalities, or enable unprecedented
behavioral monitoring surveillance that challenges the fundamental values of
free, democratic societies.
Given that AI is no longer solely the domain of technologists but rather of
society as a whole, we need tighter coupling of computer science and those
disciplines that study society and societal values.

人工智能创新主要关注 “what” 和 “how” 问题，忽视可能的危害与社会背景，导致了潜在的社会技术问题，因此我们需要计算机科学和社会学科之间更紧密的联系。

跨学科方法探讨人工智能对社会影响的理解

Interdisciplinary Approaches to Understanding Artificial Intelligence's  Impact on Society

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.

本篇论文利用复杂自适应系统、协作因果理论形成和以社区为基础的系统动力学三种方法来帮助人们更好地理解、识别和表达社会背景，提出了如何通过系统论方法来改进机器学习的公平性，包括将机器学习公平性的抽象范围扩展至社会背景等方面，以实现公平和包容的机器学习产品的开发。