Attrition models combine variables into statistical algorithms to understand and predict employee turnover. People analytics teams and external vendors use attrition models to offer insights and to develop organisational interventions. However, if attrition models or other data-driven models inform employment decisions, model scores may then be subjected to civil rights laws and diversity concerns resulting from group differences in scores. This paper discusses adverse impact when building attrition models, outlining how researchers test for adverse impact in this context, strategies to reduce group differences and how attrition modelling and other human resources ‘big data’ predictions fit within larger validity frameworks. Procedures were applied to field data in an applied demonstration of an attrition model with disparate impact. Model revisions resulted in adverse impact reductions while simultaneously maintaining model validity. Collectively, this paper provides timely attention to important aspects of the people analytics, turnover and legal domains.

中文翻译：

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经验消耗模型和歧视：平衡有效性和群体差异

损耗模型将变量结合到统计算法中，以了解和预测员工流动率。人员分析团队和外部供应商使用流失模型来提供见解并制定组织干预措施。然而，如果消耗模型或其他数据驱动模型为就业决策提供信息，则模型分数可能会受到民权法和由于分数的群体差异而导致的多样性问题的影响。本文讨论了构建消耗模型时的不利影响，概述了研究人员如何测试这种情况下的不利影响、减少群体差异的策略以及消耗模型和其他人力资源“大数据”预测如何适应更大的有效性框架。在具有不同影响的损耗模型的应用演示中，将程序应用于现场数据。模型修订减少了不利影响，同时保持了模型的有效性。总的来说，本文及时关注了人员分析、人员流动和法律领域的重要方面。