AHFE Open AccessTrusting the Machine – The Role of Gender and Personality in Shaping the Propensity to Trust Artificial Intelligence
Abstract
With the growing significance of Digital Trust in the context of Artificial Intelligence (AI), it is essential to identify the factors that shape individuals' propensity to place trust in AI. This study examines whether gender differences exist in the propensity to trust AI and explores the extent to which personality traits serve as significant predictors of trust. Data from N = 114 students was collected using validated psychometric questionnaires. A one-way analysis of variance (ANOVA) was used to analyze gender differences, while a multiple linear regression analysis was used to examine the influence of personality traits. The results revealed no significant gender difference. However, the personality traits conscientiousness and neuroticism were significant negative predictors of the propensity to trust AI. Overall, the Big Five personality traits explained a moderate amount of the variance in the propensity to trust AI. The findings underscore the multifaceted nature of psychological factors influencing trust in AI and contribute to the expanding body of interdisciplinary research aimed at systematically understanding this complex phenomenon.
Keywords: Artificial Intelligence, Human-AI interaction, Trust, Propensity to Trust, Trustworthy AI
DOI: 10.54941/ahfe1006734
Cite this paper
More from this volume
- User experience evaluation of an AI-based decision-support tool for power grid congestion management
- Applying Job Design Criteria for Effective Human-AI Collaboration
- Exploring and Understanding Neurodiverse Sensory Experiences and Management Through Digital Intervention
- User-Centered Design and Usability Evaluation of a Floodwater Depth Estimation Mobile Application
- Designing with ‘Intelligence’? Exploring the Limits of AI-Based UX Tools
- Multibody Simulation Framework for Human-Machine Interaction in Impact Wrench Fastening: Enabling Reliability and Work-Related Health Risk Assessment
- Generative AI for Sustainable and Efficient Layout Designs
- Machine Learning for User-Dependent Ankle Joint Torque Estimation: An Application of XGBoost
- Commute time analysis using mobile location information
- Creating Safer Learning Environments Through Universal Design for Learning Framework
- Integrating Model-Based Systems Engineering and Stakeholder-Driven Design Exploration: A Virtual Reality Approach for Early-Stage System Development
- Digital vulnerabilities and the oldest-old