在人工智能机器学习模型中嵌入透明度:预测和解释员工流失的管理含义

Soumyadeb Chowdhury, Sian Joel-Edgar, P. Dey, S. Bhattacharya, Alexander Kharlamov
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引用次数: 6

摘要

摘要员工流失是企业在各个业务部门面临的一个重大问题。人工智能(AI)机器学习(ML)预测模型可以使用历史员工数据集帮助对员工自愿离职的可能性进行分类。然而,这些基于人工智能的ML模型产生的输出响应缺乏透明度和可解释性,这使得人力资源经理很难理解人工智能预测背后的基本原理。如果管理人员不理解基于输入数据集的人工智能模型如何以及为什么产生响应,则不太可能增强数据驱动的决策并为组织带来价值。本文的主要目的是展示局部可解释模型不可知论解释(LIME)技术的能力,该技术可以直观地向人力资源经理解释基于ai的ML模型为给定员工数据集生成的ET预测。从理论角度来看,我们通过对人工智能算法透明度的概念回顾,然后利用资源基础观点理论的原则讨论其对维持竞争优势的重要性,从而为国际人力资源管理文献做出贡献。我们还使用LIME提供了一个透明的人工智能实施框架,这将为人力资源经理提供有用的指导,以提高基于人工智能的ML模型的可解释性,从而减轻数据驱动决策中的信任问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover
Abstract Employee turnover (ET) is a major issue faced by firms in all business sectors. Artificial intelligence (AI) machine learning (ML) prediction models can help to classify the likelihood of employees voluntarily departing from employment using historical employee datasets. However, output responses generated by these AI-based ML models lack transparency and interpretability, making it difficult for HR managers to understand the rationale behind the AI predictions. If managers do not understand how and why responses are generated by AI models based on the input datasets, it is unlikely to augment data-driven decision-making and bring value to the organisations. The main purpose of this article is to demonstrate the capability of Local Interpretable Model-Agnostic Explanations (LIME) technique to intuitively explain the ET predictions generated by AI-based ML models for a given employee dataset to HR managers. From a theoretical perspective, we contribute to the International Human Resource Management literature by presenting a conceptual review of AI algorithmic transparency and then discussing its significance to sustain competitive advantage by using the principles of resource-based view theory. We also offer a transparent AI implementation framework using LIME which will provide a useful guide for HR managers to increase the explainability of the AI-based ML models, and therefore mitigate trust issues in data-driven decision-making.
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