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Künstliche Intelligenz und Schweizer Recht, Januar-Dezember 2025
No full textEntsprechend dem Ziel der «(Zeit-)schriften des Rechts», die von Tag zu Tag anschwellenden und zunehmend verstreuten Veröffentlichungen zum Recht zu sammeln, zu sichten und hieraus eine Auswahl aktuell besonders lesenswerter Texte zu treffen, wird in dieser Ausgabe eine Auswahl der Schriften an der Schnittstelle von Künstlicher Intelligenz (KI) und Schweizer Recht aus dem Zeitraum Januar-Dezember 2025 vorgelegt.Entsprechend dem Ziel der «(Zeit-)schriften des Rechts», die von Tag zu Tag anschwellenden und zunehmend verstreuten Veröffentlichungen zum Recht zu sammeln, zu sichten und hieraus eine Auswahl aktuell besonders lesenswerter Texte zu treffen, wird in dieser Ausgabe eine Auswahl der Schriften an der Schnittstelle von Künstlicher Intelligenz (KI) und Schweizer Recht aus dem Zeitraum Januar-Dezember 2025 vorgelegt
In vivo pilot study on toothbrush filament end-rounding and surface integrity using 360° microscopy
No full textThis pilot study investigates the morphological changes and end-rounding quality of polyamide 6.12 (PA 6.12) toothbrush filaments over six months of regular use. Ten adult participants (N=10) used standardized manual toothbrushes twice a day, and filament tips were sampled at four intervals: baseline, 90, 180, and 360 brushing cycles. Using 360° rotational microscopy, the study assessed filament geometry and surface residues. Results showed a decline in acceptably rounded filaments decreased from 64% at baseline to 29% after 360 cycles, whereas surface residues decreased significantly from 87% in the unused state to 17% and 18% after 180 and 360 brushing cycles. Rotational analysis revealed asymmetrical wear and angle dependent damage, often missed by static imaging. These findings suggest that residues on new toothbrushes may originate from manufacturing processes or micro-plastic contamination, highlighting the importance of rinsing new brushes before use. The study demonstrates that longitudinal in vivo evaluation with 360° microscopy enables precise assessment of surface residues and tip rounding quality under real-world brushing conditions
Ireland’s First Undergraduate Animal Law Module in a Law School
No full textThis article is an account of how I came to set up Ireland’s first module in animal law at undergraduate level in a law school. I give an insight into my multidisciplinary and international academic background which sheds light on my influences. I also talk about how I became vegetarian and later vegan and my involvement with the Vegetarian Society of Ireland. At one point, I was determined not to develop an academic interest in animal law, but I was often asked to give talks about vegetarianism and veganism, human rights, genetically modified animals, cultivated meat and animals so eventually I resigned myself and gave in to the inevitable. There were three catalysts which decided when this would be: I was invited to become a Fellow of the Oxford Centre for Animal Ethics in 2014 and have presented and published with the Centre ever since; after Brexit I stopped teaching English Land Law; and, the Cambridge Centre for Animal Rights Law invited me to do their teaching workshop in Antwerp in 2022. My very supportive Head of School, Dr. Charles O’Mahony encouraged me to propose the module soon afterwards, it was approved and run for the first time in 2025.This article is an account of how I came to set up Ireland’s first module in animal law at undergraduate level in a law school. I give an insight into my multidisciplinary and international academic background which sheds light on my influences. I also talk about how I became vegetarian and later vegan and my involvement with the Vegetarian Society of Ireland. At one point, I was determined not to develop an academic interest in animal law, but I was often asked to give talks about vegetarianism and veganism, human rights, genetically modified animals, cultivated meat and animals so eventually I resigned myself and gave in to the inevitable. There were three catalysts which decided when this would be: I was invited to become a Fellow of the Oxford Centre for Animal Ethics in 2014 and have presented and published with the Centre ever since; after Brexit I stopped teaching English Land Law; and, the Cambridge Centre for Animal Rights Law invited me to do their teaching workshop in Antwerp in 2022. My very supportive Head of School, Dr. Charles O’Mahony encouraged me to propose the module soon afterwards, it was approved and run for the first time in 2025
Visual Framing in the AI Era: Lessons from Manual Approaches for Computational Methods
No full textComputational methods can minimize the time and resources needed to manually code thousands of images. Yet, they also come with challenges, including validation, algorithmic bias, and privacy concerns. Acknowledging that the pictorial turn has now entered a computational phase, this article reports on a manual and automated coding of 7000+ images to better understand online extremist content. Using Rodriguez and Dimitrova’s (2011) four-tiered model of visual framing, the study compares manual and OpenAI’s ChatGpt4o’s coding of Al-Qaeda and ISIS images across the denotative, semiotic, connotative, and ideological levels. AI coding exhibited moderate to strong performance on denotative variables but was weaker in the semiotic and connotative tiers. The study concludes with a discussion of the advantages of human and AI functioning together to better understand visual framing
Visual Framing at Scale: A Theory-Driven Computational Framework for Analyzing Protest Imagery with Generative AI
No full textThis study presents a theory-driven, three-stage computational framework for analyzing visual framing in protest imagery. Focusing on the Black Lives Matter movement, we examine how visual elements contribute to two well-established frames in protest media coverage: the protest paradigm and solidarity framing. Leveraging GPT-4o and OpenCV, our framework extracts denotative and semiotic features—such as police presence, contestation, solidarity actions, and color contrast—and links these features to higher-order frame classifications using interpretable logistic regression models. The framework includes: (1) feature definition and validation through generative AI and a feature extraction tool, supported by human coders; (2) model training; and (3) predictive application to unseen images. Results show strong alignment between human and machine annotations, as well as high predictive accuracy in identifying the protest paradigm or solidarity frame in BLM images. We also introduce an intra-prompt stability score for the generative AI model to help mitigate hallucination and enhance the reliability of its outputs. This study offers a scalable, replicable, and interpretable approach to visual framing analysis, bridging communication theory with advanced computational tools in the study of visual political communication
Mapping the e-petition ecosystem through Social Media: mobilization in the EU across issues and ideologies
No full textWe study petition-related mobilization on Social Media across issues and ideologies. Using calls to sign petitions on X in the seven most spoken EU languages, we build the first multi-platform, multi-language map of the e-petitioning ecosystem. To ensure cross-language and cross-ideology comparability, we infer call for signatures’ issues using ManifestoBERTa and users\u27 ideological orientation via Ideology Scaling methods calibrated using expert survey data. We classify active individuals into an ontology of mobilization types. Results show that e-petition activism is issue-specific and short-lived, with only a small portion of individuals engaging in sustained mobilization. We characterize differences in e-activism across the Left-Right spectrum, with Right-leaning users being most active on issues like political corruption and traditional morality, while environmental protection sees low engagement levels compared to its reach
Positivity Bias in AI-Generated Summaries of User-Generated Content: Exploring Its Sources and Impact on Public Sentiment
No full textRecently, platforms have been increasingly deploying generative AI (GenAI) to summarize user-generated content (UGC) into AI-generated summaries (AIGS). However, the potential bias in AIGS and its impact on the public remain inadequately examined. We used Weibo, a leading social media in China, as a case to investigate these important questions, focusing on public sentiments. Specifically, we explored whether AIGS are biased in representing emotions in UGC and whether such representation influences subsequent public sentiment. We empirically identify two sources of bias in the algorithmic processes underlying the production of AIGS from UGC: the sampling process, in which GenAI selects a subset of UGC, and the summarizing process, in which the summary is generated from the sampled content. Comparing emotions in AIGS, sampled UGC, and all UGC, we found evidence of bias in both processes. In our case, GenAI tends to favor positive UGC during the sampling process and produces summaries that further amplify this positivity, leading to an over-representation of positive sentiments in AIGS. Additionally, we utilized a Difference-in-Differences (DiD) design to explore AIGS in public sentiment dynamics. Findings suggest that AIGS alone are insufficient to influence public sentiment significantly. Overall, this study provides important implications for deploying GenAI in public online discussions
Just Another Hour on TikTok: ID sampling to obtain a complete slice of TikTok
No full textTikTok is now a massive platform, and has a deep impact on global events. Despite preliminary studies, issues remain in determining fundamental characteristics of the platform. We develop a method to extract a representative sample of >99% of posts from a given time range on TikTok, and use it to collect all posts from a full hour on the platform, alongside all posts from a single minute from each hour of a day. Through this, we obtain post metadata, video media, and comments from a close-to-complete slice of TikTok, and report the critical statistics of the platform. Notably, we estimate a total of 269 million posts produced on the day we looked at, that 18% of videos on the platform feature children, and that at least 0.5% of posts contain artificial intelligence-generated content