From Prompts to Trustworthiness: Operationalizing Technological Transparency in Generative AI Systems

Compared to traditional information systems, generative AI (GenAI) systems pose unique challenges for user trustworthiness perception due to their opaque architectures and open-ended outputs. This study conceptualizes technological transparency as a multidimensional concept encompassing data source transparency, algorithmic transparency, and limitation transparency. We operationalize this concept through a prompt-based intervention that conditions a GenAI assistant to embed transparency cues directly into its responses. In a randomized experiment with undergraduate students using the assistant for career exploration, we compare a technologically transparent condition with a control condition with outcomes on performance risk and trustworthiness perception. Results show that the intervention significantly reduces users’ performance risk perception and increases their trustworthiness perception of the system. Mediation analysis further reveals that performance risk perception fully mediates the effect of technological transparency on trustworthiness perception. Together, these findings advance theory by clarifying how technological transparency shapes user evaluations in GenAI contexts and offer a scalable, low-cost strategy for fostering trustworthy GenAI through prompt engineering.