When deciding on social welfare benefits, street-level bureaucrats wield significant discretionary power over citizens. One of the key instruments of this power lies in the questioning patterns that control the conversational agenda in face-to-face encounters. In turn, the citizens’ responses show how they navigate these conversational constraints, for instance by answering directly or through more evasive strategies. To shed light on the power dynamics inherent in these encounters, we provide over 200 verbatim transcripts of authentic conversations in German between street-level bureaucrats and citizens, as well as a fully annotated dataset of all question-response pairs extracted from these conversations. We also present PSE v2.0, which is double the size of the only previously available corpus of spoken interactions in street-level bureaucracy. Keywords: Public Service Encounters, verbatim transcripts, question-response pairs

Face-to-face interactions between representatives of the state and citizens are a key intercept in public service delivery, for instance when providing social benefits to vulnerable groups. Despite the relevance of these encounters for the individual, but also for society at large, there is a significant research gap in the systematic empirical study of the communication taking place. This is mainly due to the high institutional and data protection barriers for collecting data in a very sensitive and private setting in which citizens request support from the state. In this paper, we describe the procedure of compiling the first open access dataset of transcribed recordings of so-called Public Service Encounters in Germany, i.e., meetings between state officials and citizens in which there is direct communication in order to allocate state services. This dataset sets a new research directive in the social sciences, because it allows the community to open up the black box of direct state-citizen interaction. With data of this kind it becomes possible to directly and systematically investigate bias, bureaucratic discrimination and other power-driven dynamics in the actual communication and ideally propose guidelines as to alleviate these issues.

During Parole Suitability Hearings commissioners need to evaluate whether an inmate’s risk of reoffending has decreased sufficiently to justify their release from prison before completing their full sentence. The conversation between the commissioners and the inmate is the key element of such hearings and is largely driven by question-and-answer patterns which can be influenced by the commissioner’s questioning behavior. To our knowledge, no previous study has investigated the relationship between the types of questions asked during parole hearings and potentially biased outcomes. We address this gap by analysing commissioner’s questioning behavior during Californian parole hearings. We test ChatGPT-4o’s capability of annotating questions automatically and achieve a high F1-score of 0.91 without prior training. By analysing all questions posed directly by commissioners to inmates, we tested for potential biases in question types across multiple demographic variables. The results show minimal bias in questioning behavior toward inmates asking for parole.