Artificial intelligence in the workplace – A double-edged sword

知识管理 能力(人力资源) 计算机科学 组织学习 人工智能 互补性(分子生物学) 管理科学 心理学 工程类 社会心理学 生物 遗传学 操作系统
作者
Uta Wilkens
出处
期刊:Campus-wide Information Systems [Emerald Publishing Limited]
卷期号:37 (5): 253-265 被引量:69
标识
DOI:10.1108/ijilt-02-2020-0022
摘要

Purpose The aim of this paper is to outline how artificial intelligence (AI) can augment learning process in the workplace and where there are limitations. Design/methodology/approach The paper is a theoretical-based outline with reference to individual and organizational learning theory, which are related to machine learning methods as they are currently in use in the workplace. Based on these theoretical insights, the paper presents a qualitative evaluation of the augmentation potential of AI to assist individual and organizational learning in the workplace. Findings The core outcome is that there is an augmentation potential of AI to enhance individual learning and development in the workplace, which however should not be overestimated. AI has a complementarity to individual intelligence, which can lead to an advancement, especially in quality, accuracy and precision. Moreover, AI has a potential to support individual competence development and organizational learning processes. However, a further outcome is that AI in the workplace is a double-edged sword, as it easily shows reinforcement effects in individual and organizational learning, which have a backside of unintended effects. Research limitations/implications The conceptual outline makes use of examples for illustrating phenomenon but needs further empirical analysis. The research focus on the meso level of the workplace does not fully refer to macro level outcomes. Practical implications The practical implication is that it is a matter of socio-technical job design to integrate AI in the workplace in a valuable manner. There is a need to keep the human-in-the-loop and to complement AI-based learning approaches with non-AI counterparts to reach augmentation. Originality/value The paper faces workplace learning from an interdisciplinary perspective and bridges insights from learning theory with methods from the machine learning community. It directs the social science discourse on AI, which is often on macro level to the meso level of the workplace and related issues for job design and therefore provides a complementary perspective.
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