University of Twente Student Theses


Automatic Personality Prediction Based on Facial Features: Race, Gender, and Age Bias

Keszler, N.S. (2021) Automatic Personality Prediction Based on Facial Features: Race, Gender, and Age Bias.

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Abstract:Technological innovations change the way we live and work, how we perceive others and ourselves. Latest research in the personality assessment industry has considered machine learning for job screening processes. A specific, yet less investigated method for fully automated personality prediction is offered through facial recognition systems. In the context of machine learning algorithms for personality prediction, compliance with modern Diversity, Equity, and Inclusion standards of the industry must be considered. Hence, in this causal-comparative study, previously developed deep learning models for personality prediction based on facial features were tested for influence of race, gender, and age on the correctness of classifying Extraversion and Conscientiousness using logistic regression analyses. Hereby, the research question ‘To what extent does race, gender and age influence the prediction of Extraversion and Conscientiousness through an FRS?’ is aimed at to be answered. Two stratified samples were used, with 75 and 85 participants respectively. None of the predictor variables showed a significant influence on correctness of prediction for either trait. This leads to the conclusion that the algorithm predicts Extraversion and Conscientiousness in an unbiased manner. For future research, it is advised to further validate the algorithm on new data, continuous variables, and other personality traits.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology BSc (56604)
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