Between transparency and trust: identifying key factors in AI system perception

With the deployment of AI systems across multiple domains, understanding how users develop trust has becomecrucial for successful implementation. This study investigates how different AI features influence the decision to use an AI system and which characteristics users prioritise when evaluating them. We focus on whether users prefer systems whose functioning they can understand or whose trustworthiness is certified. We examined whether users favour system transparency through explainability features or rely more on external trust signals, such as AI certification seals, while considering how these preferences interact with technical reliability and fairness. Using conjoint analysis, we systematically compared the influence of four key attributes (transparency by explainability features, technical reliability, external trust signals through AI certifications, and fairness) on user decisions to use an AI system. Through cluster analysis, we identified two groups with opposing preferences and demographic differences. The first group prioritised high explainability and strong AI certification while showing negative preferences for fairness, whereas the second group favoured fairness and reliability while displaying negative attitudes toward explainability and AI certification. These contrasting prioritisation patterns raise important questions about AI systems development, particularly regarding challenges of addressing competing user requirements for trust-related features.