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Explainable Personality Prediction Using Answers to Open-Ended Interview Questions.

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This study shows that analyzing job applicant interview answers can infer personality traits using a model called InterviewBERT. This method offers explainable personality assessment from text, improving accessibility and reducing bias.

Keywords:
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Area of Science:

  • Psychology
  • Natural Language Processing
  • Machine Learning

Background:

  • Personality assessment is crucial in hiring.
  • Traditional methods can be subjective and time-consuming.
  • Automated analysis of text-based responses offers a potential solution.

Purpose of the Study:

  • To develop and validate a model for inferring personality traits from job interview text.
  • To leverage a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, InterviewBERT.
  • To ensure the model's explainability for personality inference.

Main Methods:

  • Analyzed text responses from over 58,000 job applicants.
  • Utilized a fine-tuned InterviewBERT model trained on a large corpus of interview answers.
  • Correlated language-inferred HEXACO personality traits with self-rated scores.

Main Results:

  • Achieved an average correlation of r = 0.37 across six HEXACO dimensions between inferred and self-rated personality traits.
  • Highest correlation (r = 0.45) observed for Openness, lowest (r = 0.28) for Agreeableness.
  • Inferred gender differences in personality align with findings from traditional self-report inventories.

Conclusions:

  • InterviewBERT can infer personality traits from interview text in an explainable manner.
  • This approach enhances the accessibility of personality assessments.
  • Automated text analysis reduces subjective bias inherent in human judgment of candidate personality.