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Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions

  • Heysem Kaya
  • Albert Ali Salah
Chapter
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

Automatic analysis of job interview screening decisions is useful for establishing the nature of biases that may play a role in such decisions. In particular, assessment of apparent personality gives insights into the first impressions evoked by a candidate. Such analysis tools can be used for training purposes, if they can be configured to provide appropriate and clear feedback. In this chapter, we describe a multimodal system that analyzes a short video of a job candidate, producing apparent personality scores and a prediction about whether the candidate will be invited for a further job interview or not. This system provides a visual and textual explanation about its decision, and was ranked first in the ChaLearn 2017 Job Candidate Screening Competition. We discuss the application scenario and the considerations from a broad perspective.

Keywords

Explainable machine learning Job candidate screening Multimodal affective computing Personality trait analysis 

Notes

Acknowledgements

This work is supported by Boğaziçi University Project BAP 16A01P4 and by the BAGEP Award of the Science Academy.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringNamik Kemal UniversityCorluTurkey
  2. 2.Department of Computer EngineeringBogazici UniversityIstanbulTurkey
  3. 3.Future Value Creation Research CenterNagoya UniversityNagoyaJapan

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