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负责任人工智能中的杠杆作用区:迈向系统思维概念化

Leverage zones in Responsible AI: towards a systems thinking conceptualization.

作者信息

Nabavi Ehsan, Browne Chris

机构信息

Responsible Innovation Lab, Center for Public Awareness of Sciences, The Australian National University, Canberra, ACT Australia.

出版信息

Humanit Soc Sci Commun. 2023;10(1):82. doi: 10.1057/s41599-023-01579-0. Epub 2023 Mar 4.

Abstract

There is a growing debate amongst academics and practitioners on whether interventions made, thus far, towards Responsible AI have been enough to engage with the root causes of AI problems. Failure to effect meaningful changes in this system could see these initiatives not reach their potential and lead to the concept becoming another buzzword for companies to use in their marketing campaigns. Systems thinking is often touted as a methodology to manage and effect change; however, there is little practical advice available for decision-makers to include systems thinking insights to work towards Responsible AI. Using the notion of 'leverage zones' adapted from the systems thinking literature, we suggest a novel approach to plan for and experiment with potential initiatives and interventions. This paper presents a conceptual framework called the Five Ps to help practitioners construct and identify holistic interventions that may work towards Responsible AI, from lower-order interventions such as short-term fixes, tweaking algorithms and updating parameters, through to higher-order interventions such as redefining the system's foundational structures that govern those parameters, or challenging the underlying purpose upon which those structures are built and developed in the first place. Finally, we reflect on the framework as a scaffold for transdisciplinary question-asking to improve outcomes towards Responsible AI.

摘要

学术界和从业者之间对于迄今为止针对负责任人工智能所采取的干预措施是否足以解决人工智能问题的根源存在越来越多的争论。如果未能在这个系统中实现有意义的变革,可能会导致这些举措无法发挥其潜力,并使这个概念成为公司在营销活动中使用的又一个流行语。系统思维常常被吹捧为一种管理和实现变革的方法;然而,对于决策者来说,几乎没有实用的建议可供他们将系统思维的见解纳入到朝着负责任人工智能努力的工作中。借鉴系统思维文献中提出的“杠杆作用区”概念,我们提出了一种新颖的方法来规划和试验潜在的举措与干预措施。本文提出了一个名为“五个P”的概念框架,以帮助从业者构建和识别可能有助于实现负责任人工智能的整体干预措施,从诸如短期修复、调整算法和更新参数等低阶干预措施,到诸如重新定义控制系统参数的基础结构,或者首先挑战这些结构所基于的根本目的等高阶干预措施。最后,我们将该框架视为一个跨学科提问的支架,以改善朝着负责任人工智能发展的成果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f893/9984750/79a342c93cf1/41599_2023_1579_Fig1_HTML.jpg

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