Coordinating Human and Machine Learning

Owned by Darlene Edgar
Nov 23, 2023
2 min read

Coordinating human and machine learning for effective organizational learning (Sturm et al. 2021)

  • Machine Learning Overview:

    • Involves deriving patterns from data using learning algorithms for real-world problem-solving.

    • Common types include supervised, unsupervised, and reinforcement learning.

    • Hybrid approaches combining human and machine learning are discussed.

  • Organizational Learning:

    • Organizational learning relies on a complex system of interacting learners.

    • Human learning is a social process, while machine learning is based on data processing.

    • Exploration and exploitation are key concepts influencing belief diversity within organizations.

  • Role of IT:

    • IT can facilitate or hinder knowledge creation, application, and dissemination.

    • IT is not just a tool for human learning; it can also support machine learning.

  • Components of Machine Learning:

    • Involves a narrow, real-world problem, learning algorithms (e.g., neural networks), and hyper-parameters.

    • Human setup is crucial; poorly done setups can introduce biased ideas into the system.

    • Reconfiguration is needed to align machine learning systems with current human problem understanding.

  • Learning Myopia and Human Preferences:

    • Humans naturally prefer known solutions over new, unproven ideas (learning myopia).

    • Machine learning acts as a remedial factor, allowing humans to learn according to their preferences.

    • Machine learning systems' capabilities can free up time for human activities.

  • Effectiveness of Organizational Learning:

    • Reconfiguration is essential for aligning machine learning with human understanding.

    • Human exploration is time-consuming, while machine learning swiftly discovers patterns.

    • Turbulent environments can render organizational knowledge obsolete.

  • Coordination of Human and Machine Learning:

    • Human exploration is initially superior, but machine learning agents equalize learning rates.

    • Machine learning agents with low initial learning capability depend on human exploration for diversity.

    • Machine learning agents with high initial learning capability reduce the need for human exploration without sacrificing long-term effectiveness.

  • Reconfiguration Intensity:

    • Higher reconfiguration intensity leads to higher long-term organizational knowledge.

    • Human learning behavior moderates the effects of reconfiguration intensity on organizational learning effectiveness.

    • In turbulent environments, human exploration is crucial for long-term organizational learning.

  • Considerations for Reconfiguration:

    • Reconfiguration is required to acquire high levels of organizational knowledge.

    • Humans should not be excluded from the loop, and domain expertise is crucial.

    • Moderate reconfiguration is recommended; excessive reconfiguration can be harmful.