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Comparing Machine Learning Correlations to Domain Experts’ Causal Knowledge: Employee Turnover Use Case

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Machine Learning and Knowledge Extraction (CD-MAKE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13480))

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Abstract

This paper addresses two major phenomena, machine learning and causal knowledge discovery in the context of human resources management. First, we examine previous work analysing employee turnover predictions and the most important factors affecting these predictions using regular machine learning (ML) algorithms, we then interpret the results concluded from developing and testing different classification models using the IBM Human Resources (HR) data. Second, we explore an alternative process of extracting causal knowledge from semi-structured interviews with HR experts to form expert-derived causal graph (map). Through a comparison between the results concluded from using machine learning approaches and from interpreting findings of the interviews, we explore the benefits of adding domain experts’ causal knowledge to data knowledge. Recommendations are provided on the best methods and techniques to consider for causal graph learning to improve decision making.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/microsoft-teams/group-chat-software.

  2. 2.

    https://lucid.co/.

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Authors and Affiliations

  1. Department of Computing, University of Worcester, Worcester, WR1 3AS, UK

    Eya Meddeb & Christopher Bowers

  2. Worcester Business School, University of Worcester, Worcester, WR1 3AS, UK

    Eya Meddeb, Christopher Bowers & Lynn Nichol

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  1. Eya Meddeb
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  2. Christopher Bowers
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  3. Lynn Nichol
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Correspondence to Eya Meddeb .

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Editors and Affiliations

  1. University of Natural Resources and Life Sciences Vienna, Vienna, Austria

    Andreas Holzinger

  2. St. Pölten University of Applied Sciences, St. Pölten, Austria

    Peter Kieseberg

  3. TU Wien, Vienna, Austria

    A Min Tjoa

  4. SBA Research, Vienna, Austria

    Edgar Weippl

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Meddeb, E., Bowers, C., Nichol, L. (2022). Comparing Machine Learning Correlations to Domain Experts’ Causal Knowledge: Employee Turnover Use Case. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2022. Lecture Notes in Computer Science, vol 13480. Springer, Cham. https://doi.org/10.1007/978-3-031-14463-9_22

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  • DOI: https://doi.org/10.1007/978-3-031-14463-9_22

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