Modeling Personality vs. Modeling Personalidad

Abstract

Sensor data collected from smartphones provides the possibility to passively infer a user's personality traits. Such models can be used to enable technology personalization, while contributing to our substantive understanding of how human behavior manifests in daily life. A significant challenge in personality modeling involves improving the accuracy of personality inferences, however, research has yet to assess and consider the cultural impact of users' country of residence on model replicability. We collected mobile sensing data and self-reported Big Five traits from 166 participants (54 women and 112 men) recruited in five different countries (UK, Spain, Colombia, Peru, and Chile) for 3 weeks. We developed machine learning based personality models using culturally diverse datasets -representing different countries -and we show that such models can achieve state-of-the-art accuracy when tested in new countries, ranging from 63% (Agreeableness) to 71% (Extraversion) of classification accuracy. Our results indicate that using country-specific datasets can improve the classification accuracy between 3% and 7% for Extraversion, Agreeableness, and Conscientiousness. We show that these findings hold regardless of gender and age balance in the dataset. Interestingly, using gender-or age-balanced datasets as well as gender-separated datasets improve trait prediction by up to 17%. We unpack differences in personality models across the five countries, highlight the most predictive data categories (location, noise, unlocks, accelerometer), and provide takeaways to technologists and social scientists interested in passive personality assessment.

The relevance of perceived work environment and work activities for personality trajectories in midlife

“…Furthermore, retirement can be a transitional phase potentially triggering personality development (Löckenhoff et al, 2009; Robinson et al, 2010; Schwaba & Bleidorn, 2019). Regional differences should also be taken into account because socialization in different contexts may potentially affect both work environments and personality (Allik et al, 2017; Khwaja et al, 2019; Murphy et al, 2021).…”

Two edges of the screen: Unpacking positive and negative associations between phone use in everyday contexts and subjective well-being

“…Further, we used the median value to split participants into two groups, e.g., into High Agreeableness versus Low Agreeableness. This methodology has been previously applied in the literature [ 50 , 51 ] to analyse different clusters of individuals. The exhaustive categories considered for our analysis are shown in Table 1 .…”

Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing

“…7 Post data collection, Su et al [125] performed data balancing, conditioned on the sensitive attribute, managing to narrow the impact of gender voice differences on their speech recognition model. Similarly, a strand of work explored data splitting, conditioned on the sensitive attribute (gender, age, BMI, skin tone, country, and health condition) to enable model personalization [59,77,91,125,150]. More advanced mechanisms suggest modifying feature representations so that a subsequent classifier will be fairer.…”