Data, Trust, and Ethics: : A Quantitative Study of Consumers’ Perceptions and Trust in AI-Driven Data Processing under GDPR

Sammanfattning

As Artificial Intelligence (AI) driven systems become a routine part of online services from personalized recommendations to automated decision tools, questions emerge about how consumers feel when their personal data is processed. Under the EU’s General Data Protection Regulation (GDPR), organizations must uphold privacy and transparency, but it remains unclear whether users trust these protections or even understand how AI works. This thesis sets out to explore these questions by examining consumer perceptions across five key dimensions. This thesis is a quantitative study which investigates how consumers in the European Union perceive AI systems that process personal data under the GDPR. The authors conducted a cross-sectional online survey with 46 digitally active adults, measuring five key dimensions: self-reported exposure to AI, perceived understanding of AI mechanisms, trust in AI systems, demand for clear explanations, and perceived legal protection under GDPR. Using correlation analyses, the study explores relationships among these dimensions to reveal how exposure and understanding shape trust, how explainability affects confidence, and whether belief in legal safeguards influences the desire for transparency. Overall, the findings suggest that mere exposure to AI does not guarantee deeper understanding, that understanding is crucial for building trust, and that clear explanations can further strengthen confidence while legal protections alone may not satisfy users’ calls for transparency. The authors note limitations related to sample size and sampling method and recommend broader, mixed-method research in future work.