Trust in Data Science
The project investigated trust-related questions in the context of data science practice and knowledge. Two perspectives on trust were investigated in particular:
1. Trust-building measures in interdisciplinary data science
On the one hand, the project investigated trust-building measures in the context of interdisciplinary collaboration. What practices and infrastructures are relevant to establish trust-worthy relationships for interdisciplinary collaboration in data science?
2. Public trust in data knowledge
On the other hand, the project took at closer look at public perceptions of climate risk information within online media settings. How do social media users cope with uncertainty linked to risky climate futures? How do they negotiate uncertainties among each other?
The project operationalized ethnographic and digital methods with a particular focus on interdisciplinary collaboration. Its theoretical fundament lies in Science & Technology Studies (STS) as well as Critical Algorithm and Data Studies.
Exemplary research outputs
Hirsbrunner, Simon David. “Negotiating the Data Deluge on YouTube: Practices of Knowledge Appropriation and Articulated Ambiguity Around Visual Scenarios of Sea-Level Rise Futures.” Frontiers in Communication 6 (2021).
- trust, data science, machine learning, science and technology studies, climate change, risk communication, interdisciplinary collaboration