TY - GEN
T1 - A Machine Learning Framework to Identify Employees at Risk of Wage Inequality
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019
AU - Karimian, Hamid R.
AU - Rouhanizadeh, Behzad
AU - Jafari, Amirhosein
AU - Kermanshachi, Sharareh
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088111153&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85088111153
T3 - Computing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 26
EP - 34
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
Y2 - 17 June 2019 through 19 June 2019
ER -