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Dark triad personality traits prediction with AI

Published online by Cambridge University Press:  13 August 2021

A. Mereu
A. Mereu*
Affiliation:
Research performed independently, Cagliari, Italy
*
*Corresponding author.

Abstract

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Introduction

The dark triad is composed by the personality traits Machiavellianism, narcissism and psychopathy (MNP). Their complexity can make them difficult to interrelate. Artificial intelligence (AI) could help in this endeavour.

Objectives

To investigate whether AI could predict MNP from themselves.

Methods

Data from 210 questionnaires were analysed using an AI. The short Dark Triad questionnaire (SD3) was used to assess MNP. Two of the MNP scores were employed to predict the third one and the procedure was repeated for all of them alternatively. The AI was conservatively tuned to maximize the one-way random intraclass correlation coefficient (ICC) between predicted and real values. Pearson’s r was calculated too. The free and open source programming language R was used for all the analyses. Dataset source: Borráz-León, Javier I. (2020), “Dark triad, attractiveness, mate value, and sexual partners”, Mendeley Data, V1, doi: 10.17632/87vx6jfnrp.1

Results

Machiavellianism, narcissism and psychopathy predictions obtained ICC of 0.593, 0.335, 0.505 and Pearson’s r of 0.608, 0.346, 0.548 respectively. The results were indicative of fair performance, mainly for Machiavellianism and psychopathy.

Conclusions

AI might be useful to predict MNP. This could be utile in many situations, such as dealing with missing data or deciding whether to formally test someone. Finally, the AI used in this study is freely available, allowing anyone to experiment.

Disclosure

No significant relationships.

Keywords

Artificial IntelligencePersonalitytraitspsychometry
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S140 - S141
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
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