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Technological Workforce and Its Impact on Algorithmic Justice in Politics

Author

Listed:
  • Jerome D. Williams

    (Rutgers-The State University of New Jersey)

  • David Lopez

    (Rutgers-The State University of New Jersey)

  • Patrick Shafto

    (Rutgers-The State University of New Jersey)

  • Kyungwon Lee

    (University of Michigan-Dearborn)

Abstract

The use of algorithms can be highly beneficial and efficient to make statistical decisions in settings where data are voluminous. However, there are on-going concerns about the potential long-term negative consequences of the use of algorithms due to inherent biases against certain subgroups of the population which tend to be under-represented in the society. To address this issue, we propose that it is critical to develop ways to bring the technological capabilities that underlie these advances to the broadest group of people by focusing on the nature of workforce in the tech industry. Particularly, we propose that having a diverse workforce in the tech industry and inter-disciplinary education, including principles of ethical coding, can be a starting point to resolve this issue. Politicians, regulators, and educational institutions must be prepared to address these issues in order to set a system that works equally for all people in a democratic society.

Suggested Citation

  • Jerome D. Williams & David Lopez & Patrick Shafto & Kyungwon Lee, 2019. "Technological Workforce and Its Impact on Algorithmic Justice in Politics," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 6(3), pages 84-91, December.
    • Handle: RePEc:spr:custns:v:6:y:2019:i:3:d:10.1007_s40547-019-00103-3
    DOI: 10.1007/s40547-019-00103-3
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    References listed on IDEAS

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    1. Marianne Bertrand & Sendhil Mullainathan, 2004. "Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination," American Economic Review, American Economic Association, vol. 94(4), pages 991-1013, September.
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    Cited by:

    1. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    2. Lee, Kyungwon & Hakstian, Anne-Marie & Williams, Jerome D., 2021. "Creating a world where anyone can belong anywhere: Consumer equality in the sharing economy," Journal of Business Research, Elsevier, vol. 130(C), pages 221-231.
    3. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    4. David A. Schweidel & Neil Bendle, 2019. "Marketing and Politics: Strange Bedfellows no More," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 6(3), pages 37-40, December.
    5. Wenlong Sun & Olfa Nasraoui & Patrick Shafto, 2020. "Evolution and impact of bias in human and machine learning algorithm interaction," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.

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