Privacy-Preserving Machine Learning and Consumer Trust: The Mediating Role of Data Privacy and the Moderating Role of Privacy Awareness

Abstract

In a world of 'digital platforms' characterized by numerous machine learning (ML) applications, consumers are increasingly worried about their personal data being used and tracked, and how the use of algorithms can affect transparency in these applications. This growing number of machine learning (machine learning-based) applications in digital platforms is driving consumer concern about data usage, surveillance and algorithmic opacity. For these concerns, machine learning (ML) is a technical and organizational solution that appears to offer promise, but the literature on behavioral sciences does not fully elucidate the connection between ML and consumer trust. The aim of this study is to examine how privacy-preserving machine learning practices affect consumer trust, examining data privacy as a mediator and privacy awareness as a moderator. The research design was behavioral quantitative with items obtained from 300 respondents who were users of digital platforms and 300 respondents who were consumers of AI services, which is based on a structured questionnaire that has been validated. KMO and Bartlett's test were used to verify the data adequacy. The construction and structures were confirmed by Confirmatory Factor Analysis (CFA) and the structural relations were estimated by use of Partial Least Squares Structural Equation Modeling (PLS-SEM) using bootstrapped confidence intervals. Indirect effect decomposition (Variance Accounted For (VAF)) and multi-group analysis (MGA) were the methods used to measure mediation and moderation, respectively. Model's robustness was checked by means of predictive relevance (Q²) and of effect size (f²). The results indicate PPML has significant improvement among consumers' trust, and the data privacy partially mediates this relation. Overall, privacy awareness significantly moderates the pathway between PPML and data privacy and between data privacy and consumer trust, as users who are high-aware have significantly stronger reactions to data privacy and consumer trust in relation to PPML practices. The results have significant theoretical and practical implications concerning the design and communication of AI systems that are respectful to privacy.