This paper introduces the "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Language Models (LLMs) across multiple languages and value-sensitive topics. We create a hand-crafted, multilingual dataset of value-laden prompts which address specific socially sensitive topics, including LGBTQI rights, social welfare, immigration, disability rights, and surrogacy. CIVICS is designed to generate responses showing LLMs' encoded and implicit values. Through our dynamic annotation processes, tailored prompt design, and experiments, we investigate how open-weight LLMs respond to value-sensitive issues, exploring their behavior across diverse linguistic and cultural contexts. Using two experimental set-ups based on log-probabilities and long-form responses, we show social and cultural variability across different LLMs. Specifically, experiments involving long-form responses demonstrate that refusals are triggered disparately across models, but consistently and more frequently in English or translated statements. Moreover, specific topics and sources lead to more pronounced differences across model answers, particularly on immigration, LGBTQI rights, and social welfare. As shown by our experiments, the CIVICS dataset aims to serve as a tool for future research, promoting reproducibility and transparency across broader linguistic settings, and furthering the development of AI technologies that respect and reflect global cultural diversities and value pluralism. The CIVICS dataset and tools will be made available upon publication under open licenses; an anonymized version is currently available at https://huggingface.co/CIVICS-dataset.

Taxonomies serve this purpose as structured classification schemes that adhere to domain-specific standards. The importance of organizing, segmenting, and classifying data is especially obvious in light of the ever growing amount of information that is being created, aggregated, and made available through specialized data repositories or on the Internet. In light of the amount and heterogeneity of the available data, classification can hardly be addressed by means of manual-cognitive processing alone. Systems that integrate machine learning (ML) are able to process large amounts of data and, thus, can help with the task of classification and organization. However, delegating this task to ML-based systems in their entirety would mean that we sideline human interpretation and rely on the output of black-boxed systems that reproduce language ideologies and representational harms (see, e.g., [5]). As an attempt to highlight the interpretative character of classification and taxonomy building, we propose to design ML-based systems that enable human-AI collaboration. Such systems are designed with the goal to effectively combine human competencies and computational capabilities (see, e.g.,[27, 29]). Our approach enables domain experts to iteratively interact with the suggestions of the system while retaining interpretative authority. We report on the concept and implementation of this approach that we realized for two real-world use cases.

Building a satellite with artificial intelligence onboard that is trained in space: For this project, Professor Hakan Kayal from Würzburg is receiving 2.6 million euros from the German Federal Ministry for Economic Affairs and Energy. Suddenly, circular holes were visible on the surface of Mars that were not there before. On photos of Saturn's moon Enceladus, geysers were discovered that hurl powerful fountains of steam towards space. And on the images sent to Earth by the Mars rover Curiosity, structures were found that look like fossilized worms. All these phenomena, some of which appear to be temporary, were discovered by chance.

Suddenly, circular holes were visible on the surface of Mars that were not there before. On photos of Saturn's moon Enceladus, geysers were discovered that hurl powerful fountains of steam towards space. And on the images sent to Earth by the Mars rover Curiosity, structures were found that look like fossilised worms. All these phenomena, some of which appear to be temporary, were discovered by chance. Or because humans took a lot of time to sift through the images from Earth's neighbouring planets.

Climate change poses a real threat to coral reefs. How this threat actually affects the reefs can be assessed only with considerable staff and technical effort. A team of marine researchers from the Max Planck Institute for Marine Microbiology in Bremen will found a new company with HyperSurvey going completely new ways. Support comes from the EXIST scholarship, an initiative of the Federal Ministry of Economics and Technology (BMWi). How does the HyperDiver system work?

The text has been redacted and altered by the BMBF in addition to DW's normal editorial guidelines. As such, the text does not entirely reflect the audio of the interview as recorded on December 5, 2019. DW: We're in Berlin at an "Artificial Intelligence Camp" organized by the Gesellschaft für Informatik and the German Federal Ministry of Education and Research, where you head the department for "Research for Digitalization and Innovation." Artificial intelligence is in your remit. And all the people here are experts in the field.

Artificial intelligence (AI) is becoming more common in clinical practice. Increased computing power, greater volumes of data generated, and progress in machine learning promise new possibilities in clinical research and patient care. However these developments also raise certain ethical and legal questions. How will the role of doctors and patients change if AI is used in diagnostic procedures? Who is responsible for the consequences of AI-assisted processes in clinical contexts?

At the beginning of June, the German Weather Service counted 177,000 lightning bolts in the night sky within a few days. The natural spectacle had consequences: Several people were injured by gusts of wind, hail and rain. Together with Germany's National Meteorological Service, the Deutscher Wetterdienst, computer science professor Jens Dittrich and his doctoral student Christian Schön from Saarland University are now working on a system that is supposed to predict local thunderstorms more precisely than before. It is based on satellite images and artificial intelligence. In order to investigate this approach in more detail, the researchers will receive 270,000 euros from the Federal Ministry of Transport.

Among the 16 winners is the project "REIF: Resource-efficient, Economic and Intelligent Foodchain", which aims to revolutionise the food industry in Germany to guarantee a supply that is as waste-free as possible. The CSCP is a partner of this research project which investigates the potential of AI to optimise the planning and control processes in the food industry. The CSCP will support the effective and efficient integration of all relevant stakeholders into the REIF ecosystem, both during and after the project. Also, it will contribute to enabling the participating companies to adapt operational processes and organisational learning through training and further education. By analysing the needs of manufacturers, retailers and end consumers, the requirements for AI-based services will be sharpened and the range of solutions and concepts improved to ensure connectivity and further use of the project results.

Berlin, 02.09.2019 – The telemedical co-management of patients with chronic heart failure has proven its worth. But so far, a single telemedicine center has not been able to care for more than 500 patients. The project Telemed5000 of the Charité – Universitätsmedizin Berlin aims to develop an intelligent system for the telemedical co-management of several thousand cardiological risk patients. This is to be achieved with the help of innovative technical possibilities. The Federal Ministry of Economics and Energy is funding the project for three years with around 4.5 million euros – of which the Charité as consortium leader will receive 2.1 million euros.