IRF: Institutional Repository FHNW

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Publikation
Never say never. exploring the effects of knowledge availability on agent persuasiveness in controlled physiotherapy motivation dialogues
(Frontiers Research Foundation, 2026) Vonschallen, Stephan; Häusler, Rahel; Schmiedel, Theresa; Eyssel, Friederike
Generative Social Agents (GSAs) have the capability to influence their human users through persuasive communication. On the one hand, they might motivate users to pursue positive personal goals, such as following a healthier lifestyle. On the other hand, GSAs are linked to negative outcomes like manipulation and deception. These emerge as a consequence of the fact that we only have limited control over probabilistic agent outputs. However, at the same time, GSAs manifest communicative patterns based on available knowledge. Therefore, their communication behavior can be shaped by regulating their access to such knowledge. Following this approach, we explored persuasive messages from GSAs in the context of human-robot physiotherapy motivation. We did this by comparing ChatGPT-generated responses to predefined inputs from a hypothetical patient in physiotherapy. In Study 1, we qualitatively analyzed 14 ChatGPT-generated dialogue scripts with varying knowledge configurations. In Study 2, third-party observers ( N = 27) rated a selection of these scenarios in terms of the agent's expressiveness, assertiveness, and persuasiveness. Our findings indicated that LLM-based GSAs can adopt assertive and expressive personality traits, thereby significantly enhancing perceived persuasiveness. Moreover, persuasiveness improved when information about the patient's age and past profession was available, mediated by perceived agent assertiveness and expressiveness. Context-related knowledge, e.g., regarding benefits associated with physiotherapy did not significantly impact agent persuasiveness. This might be due to the fact that the LLM we used already included such information from pre-training. Overall, the present research highlights the importance of studying autonomous GSA behavior from an empirical perspective. Particularly, future research should focus on the information that is required in order to enable and assure coherent and responsible communication with generative AI systems.
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
Implicit inference of the reionization history with higher-order statistics of the 21-cm signal
(Oxford University Press, 2026) Cerardi, Nicolas; Giri, Sambit K; Bianco, Michele; Piras, Davide; de Salis, Emmanuel; De Santis, Massimo; Selcuk Simsek, Merve; Denzel, Philipp; Hess, Kelley M; Toribio, M Carmen; Kirsten, Franz; Ghorbel, Hatem
The Epoch of Reionization (EoR), when the first luminous sources ionised the intergalactic medium, represents a new frontier in cosmology. The Square Kilometre Array Observatory (SKAO) will offer unprecedented insights into this era through observations of the redshifted 21-cm signal, enabling constraints on the Universe’s reionization history. We investigate the information content of the average neutral hydrogen fraction ($\bar{x}_{\rm HI}$) in several Gaussian (spherical and cylindrical power spectra) and non-Gaussian (Betti numbers and bispectrum) summary statistics of the 21-cm signal. Mock 21-cm observations are generated using the AA* configuration of SKAO’s low-frequency telescope, incorporating noise levels for 100 and 1000 hours. We employ a state-of-the-art implicit inference framework to learn posterior distributions of $\bar{x}_{\rm HI}$ in redshift bins centred at z = 8.0, 7.2 and 6.5, for each statistic and noise scenario, validating the posteriors through calibration tests. Using the figure of merit to assess constraining power, we find that Betti numbers alone are on average more informative than the power spectra, while the bispectrum provides limited constraints. However, combining higher-order statistics with the cylindrical power spectrum improves the mean figure of merit by ~0.25 dex (~33 % reduction in $\sigma (\bar{x}_{\rm HI})$). The relative contribution of each statistic varies with the stage of reionization. With SKAO observations approaching, our results show that combining power spectra with higher-order statistics can significantly increase the information retrieved from the EoR, maximising the scientific return of future 21-cm observations.
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
Renegotiating solidarity and deservingness during the COVID-19 pandemic. the role of frontline social work
(Policy Press, 2026) Schambron, Livia; Drilling, Matthias
During the COVID-19 pandemic, a dual dynamic became visible in welfare states: on the one hand, vulnerability intensified, particularly among people in precarious legal situations; on the other hand, inclusive measures in solidarity with groups usually excluded were expanded. In this exceptional context, notions of belonging, solidarity and (un)deservingness were renegotiated. At the heart of these processes, frontline social workers played a pivotal role. Drawing on 80 semi-structured interviews with representatives of frontline organisations in Switzerland, complemented by participant observation, we identify three key dimensions in these negotiations: belonging, solidarity and deservingness. Frontline social workers found themselves in a position where the civic engagement rationale of belonging gained new prominence. They actively contributed to shaping shifts within what Sarah Schilliger called an ‘infrastructure of solidarity’ while navigating scarce resources, moral evaluations and the ethical principles of social work. With this article, we seek to advance the understanding of the role of frontline social work in negotiation processes during times of crisis and beyond.
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
Growing in algorithmic ruins. Contamination as queer-feminist method
(Routledge, 2026) Ren, Qingyi
In data science and artificial intelligence, “data contamination” is typically treated as a technical flaw to be removed. This paper instead approaches contamination as a way to examine how data infrastructures organise and exclude difference. Drawing on feminist science studies and queer theory, it explores how data cleaning and classification embed normative assumptions about gender and sexuality. Focusing on systems, such as DeepL and the United Nations Parallel Corpus, the paper analyses mistranslations and erasures of queer language. These are not isolated errors but reveal how algorithmic systems impose fixed categories onto ambiguous meanings. Engaging with artistic practices that foreground error and glitch, the paper argues that such “contamination” exposes the limits of computational systems. These moments act as “queer ghosts,” traces that resist capture. Contamination thus becomes a queer-feminist method for engaging AI through disruption and the persistence of what does not fit.
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
06 - Präsentation