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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 behavior and situational judgement can be used to infer personality traits. We analyzed responses from over 58,000 job applicants who completed an online text-based interview that also included a per...

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Autores principales: Dai, Yimeng, Jayaratne, Madhura, Jayatilleke, Buddhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716880/
https://www.ncbi.nlm.nih.gov/pubmed/36467183
http://dx.doi.org/10.3389/fpsyg.2022.865841
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author Dai, Yimeng
Jayaratne, Madhura
Jayatilleke, Buddhi
author_facet Dai, Yimeng
Jayaratne, Madhura
Jayatilleke, Buddhi
author_sort Dai, Yimeng
collection PubMed
description In this work, we demonstrate how textual content from answers to interview questions related to past behavior and situational judgement can be used to infer personality traits. We analyzed 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 utilizes 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 contextualize 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 show 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.
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spelling pubmed-97168802022-12-03 Explainable Personality Prediction Using Answers to Open-Ended Interview Questions Dai, Yimeng Jayaratne, Madhura Jayatilleke, Buddhi Front Psychol Psychology In this work, we demonstrate how textual content from answers to interview questions related to past behavior and situational judgement can be used to infer personality traits. We analyzed 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 utilizes 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 contextualize 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 show 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. Frontiers Media S.A. 2022-11-18 /pmc/articles/PMC9716880/ /pubmed/36467183 http://dx.doi.org/10.3389/fpsyg.2022.865841 Text en Copyright © 2022 Dai, Jayaratne and Jayatilleke. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Dai, Yimeng
Jayaratne, Madhura
Jayatilleke, Buddhi
Explainable Personality Prediction Using Answers to Open-Ended Interview Questions
title Explainable Personality Prediction Using Answers to Open-Ended Interview Questions
title_full Explainable Personality Prediction Using Answers to Open-Ended Interview Questions
title_fullStr Explainable Personality Prediction Using Answers to Open-Ended Interview Questions
title_full_unstemmed Explainable Personality Prediction Using Answers to Open-Ended Interview Questions
title_short Explainable Personality Prediction Using Answers to Open-Ended Interview Questions
title_sort explainable personality prediction using answers to open-ended interview questions
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716880/
https://www.ncbi.nlm.nih.gov/pubmed/36467183
http://dx.doi.org/10.3389/fpsyg.2022.865841
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