Impacts of Machine Learning on Work
Impacts of Machine Learning on Work
复制标题
机器学习对工作的影响
DOI：
10.24251/hicss.2019.719
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
F. Bolici
中科院分区：
文献类型：
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作者：
Kevin Crowston;F. Bolici
自动化技术进步的日益普及使得解决工作如何随之变化的问题变得紧迫。专注于自动化信息任务的机器学习（ML）的应用程序，我们提出了一个简单的框架，用于识别自动化系统对任务的影响。通过对有关ML的热门新闻文章的分析，我们开发了3种模式用于ML决策支持，混合决策和完全自动化，并对各种任务和系统产生影响。我们进一步考虑一个任务的自动化可能会对其他相互依赖的任务产生影响。我们的主要结论是，设计师有一系列系统选择，并且任务自动化与工作自动化不同。
The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) that automate information tasks, we present a simple framework for identifying the impacts of an automated sys-tem on a task. From an analysis of popular press articles about ML, we develop 3 patterns for the use of ML—decision support, blended decision making and complete automation—with implications for the kinds of tasks and systems. We further consider how automation of one task might have implications for other interdependent tasks. Our main conclusion is that designers have a range of options for systems and that automation of tasks is not the same as automation of work.