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AI systems pose unknown challenges for designers, policymakers, and users which aggravates the assessment of potential harms and outcomes. Although understanding risks is a requirement for building trust in technology, users are often excluded from legal assessments and explanations of AI hazards. To address this issue we conducted three focus groups with 18 participants in total and discussed the European proposal for a legal framework for AI. Based on this, we aim to build a (conceptual) model that guides policymakers, designers, and researchers in understanding users’ risk perception of AI systems. In this paper, we provide selected examples based on our preliminary results. Moreover, we argue for the benefits of such a perspective.
[Context and motivation] Communication in distributed software development is usually supported by issue tracking systems. Within these systems, most of the communication is stored as unstructured natural language text. The natural language text, however, contains much information with respect to requirements management, e.g. discussion, clarification and prioritization of features, bugs, and refactorings. [Question] This paper investigates the information stored in the issue tracking systems of four different open-source projects. It categorizes the text and reports on the distribution of issue types and information types. [Principal ideas/results] A manual analysis of 80 issues, using a grounded approach, is conducted to derive a taxonomy of issue types and information types. Subsequently, the taxonomy is used as a codebook, to manually categorize and structure the text in another 120 issues. [Contribution] The first contribution of this paper is the taxonomy of issue and information types and the second contribution is an in-depth analysis of the natural language data and the communication. This analysis showed, for example, that information with respect to prioritization and scheduling can be found in natural language data, whether the ITS supports such tasks in a structured way or not.