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首页> 外文会议>ASCE international conference on computing in civil engineering >A Machine Learning Framework to Identify Employees at Risk of Wage Inequality: U.S. Department of Transportation Case Study
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A Machine Learning Framework to Identify Employees at Risk of Wage Inequality: U.S. Department of Transportation Case Study

机译：机器学习框架来识别有工资不平等风险的员工：美国运输部案例研究

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In the last decade, many programs have been developed to help decrease or eliminate the wage inequality in the United States; however, identifying employees who might be at risk of wage inequality remains challenging. This paper presents a framework to identify such employees in an organization, using a machine learning approach. This paper utilized the U.S. Department of Transportation (DOT) workforce demographic information to train and test the model. First, a prediction model is developed to estimate the salary range of employees based on historical data, using supervised machine learning techniques. Then a minority score is defined to determine the employees who might be in the risk of inequality, based on three factors: gender, ethnicity, and disability type. Finally, a framework is developed to identify the employees at risk of wage inequality, using the prediction salary range and minority index. The proposed framework enables employers to establish a fair wage, resulting in reduction and/or elimination of inequality challenges and their consequences in their organizations.
机译：在过去的十年中，已经制定了许多计划来帮助减少或消除美国的工资不平等。但是，确定可能面临工资不平等风险的员工仍然具有挑战性。本文提出了一种使用机器学习方法来识别组织中此类员工的框架。本文利用美国运输部（DOT）的劳动力人口统计信息来训练和测试该模型。首先，使用有监督的机器学习技术，开发一种预测模型，以基于历史数据估算员工的薪资范围。然后根据三个因素定义性别分数，以确定可能面临不平等风险的员工：性别，种族和残疾类型。最后，使用预测的工资范围和少数族裔指数，开发了一个框架来识别有工资不平等风险的雇员。拟议的框架使雇主能够确定合理的工资，从而减少和/或消除不平等挑战及其对组织的影响。

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