Trust is an essential pre-condition for the acceptance of digital infrastructures and services. Transparency has been identified as one mechanism for increasing trustworthiness. Yet, it is difficult to assess to which extent and how exactly different aspects of transparency contribute to trust, or potentially impede it in cases of overwhelming complexity of the information provided. To address these issues, we performed two initial studies to help determining the factors that influence or have impact on trust, focusing on transparency across a range of elements associated with data, data infrastructures and virtual research environments. On one hand, we performed a survey among IT experts in the field of data science focusing on quality aspects in the context of re-using and sharing open source software, assessing issues such as the need for documentation, test cases, and accountability. On the other hand, we complemented this with a set of semi-structured interviews with senior researchers to address specific issues of the degree of transparency achievable with different approaches. They include, for example, the amount of transparency we can achieve with approaches from explainable AI, or the usefulness and limitations of data provenance in determining the suitability of data for reuse and others. Specifically, we consider mechanisms on three levels, i.e. technical, process-oriented as well as social mechanisms. Starting from attributes of trust in the "analogue world", we aim to understand which of these can be applied in the digital world, how they differ, and what additional mechanisms need to be established, in order to support trust in complex socio-technological processes and their emergent results when the traditional approaches cannot be applied anymore.
https://doi.org/10.2218/ijdc.v18i1.921
| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |
