With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

中文翻译：

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协调人类和机器学习以实现有效的组织学习

随着机器学习 (ML) 的兴起，人类不再是唯一能够学习并为组织的知识储备做出贡献的人。我们研究组织如何协调人类学习和机器学习，以便作为一个整体有效地学习。基于一系列基于代理的模拟，我们发现，首先，ML 可以减少组织对旨在发现新想法的人类探索性学习的需求；其次，人类对机器学习系统的调整在很大程度上是有益的，但在某些条件下这种影响会减弱甚至变得有害；第三，依赖机器学习系统创造的知识可以促进在动荡环境中的组织学习，但这需要对这些系统的初始设置进行大量投资，并与人类充分协调。