Algorithmic and data transparency improve the user to platform trust relationship

more about how their data is processed into information? (2) How do users experience different augmentations aiming at supporting trust towards a platform? We present preliminary insights into our research, based on theoretical backgrounds in psychology, science, and technology, as well as human-computer interaction studies. Our several ongoing data collections investigate trust in user-to-platform relationships through quantitative and qualitative methods with ethnographic approaches. Our preliminary findings suggest that both data use and algorithmic transparency can improve the user’s experience of trust towards the platform. In line with previous work, important factors also include the platform’s ease-of-use, absence of marketing, and giving clear justifications for collecting and use of the data in question. As learning activities and other interactions increasingly take place online, there is a growing need to understand which factors influence trust from the user’s perspective. Trust is crucial for creating good communication and learning experiences and can be supported by opening up the algorithmic mechanisms which help the users make informed decisions about the platform's trustworthiness.