.png){kind=icon}
返回
Fusing domain knowledge with machine learning: A public sector perspective
.png){kind=icon}
OA
AI
DOI：10.1016/j.jsis.2024.101848.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial 'domain knowledge ' of experts in relevant fields with 'machine knowledge ', i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms - dubbed consolidation, algorithmic mediation, and naturalization - in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.
Keyword:
Knowledge production
Artificial Intelligence
Machine Learning
Natural Language Processing
Public Sector
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.8
论文数:
688
被引数:
4.5K
机构
引用论文
分享收藏A Dialogical Approach to the Creation of New Knowledge in Organizations.png)
ORGANIZATION SCIENCE
ORGANIZATION SCIENCEIF5.4
PREAI
分享收藏Opportunity for renewal or disruptive force? How artificial intelligence alters democratic politics
PREAI
分享收藏分享收藏分享收藏Knowing in practice: Enacting a collective capability in distributed organizingORGANIZATION SCIENCE
ORGANIZATION SCIENCEIF5.4
PREAI
分享收藏
查看更多内容