Shedding Light on the Black Box: Integrating Prediction Models and Explainability Using Explainable Machine Learning

IF 8.9 2区 管理学 Q1 MANAGEMENT
Yucheng Zhang, Yuyan Zheng, Dan Wang, Xiaowei Gu, Michael J. Zyphur, Lin Xiao, Shudi Liao, Yangyang Deng
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引用次数: 0

Abstract

In contemporary organizational research, when dealing with large heterogeneous datasets and complex relationships, statistical modeling focused on developing substantive explanations typically results in low predictive accuracy. In contrast, machine learning (ML) exhibits remarkable strength for prediction, but suffers from an unexplainable analytical process and output—thus ML is often known as a “black box” approach. The recent development of explainable machine learning (XML) integrates high predictive accuracy with explainability, which combines the advantages inherent in both statistical modeling and ML paradigms. This paper compares XML with statistical modeling and the traditional ML approaches, focusing on an advanced application of XML known as evolving fuzzy system (EFS), which enhances model transparency by clarifying the unique contribution of each modeled predictor. In an illustrative study, we demonstrate two EFS-based XML models and conduct comparative analyses among XML, ML, and statistical models with a commonly-used database in organizational research. Our study offers a thorough description of analysis procedures for implementing XML in organizational research, along with best-practice recommendations for each step as well as Python code to aid future research using XML. Finally, we discuss the benefits of XML for organizational research and its potential development.
揭示黑箱:使用可解释机器学习整合预测模型和可解释性
在当代组织研究中,当处理大型异构数据集和复杂关系时,统计建模侧重于开发实质性解释,通常导致预测准确性较低。相比之下,机器学习(ML)在预测方面表现出非凡的能力,但却存在无法解释的分析过程和输出,因此ML通常被称为“黑箱”方法。可解释机器学习(XML)的最新发展将高预测精度与可解释性结合在一起,它结合了统计建模和ML范式的固有优势。本文将XML与统计建模和传统ML方法进行了比较,重点介绍了XML的一种高级应用,即进化模糊系统(EFS),它通过阐明每个建模预测器的独特贡献来增强模型的透明度。在一项说明性研究中,我们演示了两个基于efs的XML模型,并使用组织研究中常用的数据库对XML、ML和统计模型进行了比较分析。我们的研究提供了在组织研究中实现XML的分析过程的全面描述,以及每个步骤的最佳实践建议以及Python代码,以帮助将来使用XML进行研究。最后,我们讨论了XML对组织研究的好处及其潜在的发展。
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来源期刊
CiteScore
23.20
自引率
3.20%
发文量
17
期刊介绍: Organizational Research Methods (ORM) was founded with the aim of introducing pertinent methodological advancements to researchers in organizational sciences. The objective of ORM is to promote the application of current and emerging methodologies to advance both theory and research practices. Articles are expected to be comprehensible to readers with a background consistent with the methodological and statistical training provided in contemporary organizational sciences doctoral programs. The text should be presented in a manner that facilitates accessibility. For instance, highly technical content should be placed in appendices, and authors are encouraged to include example data and computer code when relevant. Additionally, authors should explicitly outline how their contribution has the potential to advance organizational theory and research practice.
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