A Machine Learning Framework to Identify Employees at Risk of Wage Inequality: U.S. Department of Transportation Case Study

Hamid R. Karimian, Behzad Rouhanizadeh, Amirhosein Jafari, Sharareh Kermanshachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationData, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages26-34
Number of pages9
ISBN (Electronic)9780784482438
StatePublished - 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019 - Atlanta, United States
Duration: Jun 17 2019Jun 19 2019

Publication series

NameComputing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019
Country/TerritoryUnited States
CityAtlanta
Period6/17/196/19/19

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Civil and Structural Engineering

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