Explainable Personality Prediction Using Answers to Open-Ended Interview Questions - Dai, Yimeng; Jayaratne, Madhura; Jayatilleke, Buddhi (Frontiers in Psychology, 2022)
In this work, we demonstrated 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 utilised a fine-tuned version of InterviewBERT, a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model extended with a large interview answer corpus of over 3 million answers (over 330 million words). InterviewBERT is able to better contextualise interview responses based on the interview specific knowledge learnt from the answer corpus in addition to the general language knowledge already encoded in the initial pre-trained BERT. Further, the "Attention-based" learning approaches in InterviewBERT enable the development of explainable personality inference models that can address concerns of model explainability, a frequently raised issue when using machine learning models. 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. We also showed that the mean differences in inferred trait scores between male and female groups are similar to that reported by others using standard self-rated item inventories. 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.