Research paper

Explainable Personality Prediction Using Answers to Open-Ended Interview Questions

In this work, we demonstrate how textual content from answers to interview questions related to past behaviour and situational judgement can be used to infer personality traits.

We analysed responses from over 58,000 job applicants who completed an online text-based interview, that also included a personality questionnaire based on the HEXACO personality model to self-rate their personality. The inference model training utilises a fine-tuned version of InterviewBERT, a pre-trained Bi-directional Encoder Representations from Transformers (BERT) model, extended with a large interview answer corpus of over 3 million answers (over 330 million words).

We obtained an average correlation of r=0.37 (p < 0.001) across the six HEXACO dimensions, between the self-rated and the language-inferred trait scores, with the highest correlation of r=0.45 for Openness and the lowest of r=0.28 for Agreeableness.

Our results show the potential of using InterviewBERT to infer personality in an explainable manner, using only the textual content of interview responses, making personality assessments more accessible and removing the subjective biases involved in human interviewer judgement of candidate personality.

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