The OECD.AI Policy Navigator

Coordinated by the Policy Lab Digital, Work & Society at the BMAS, these guidelines were developed to ensure responsible, human-centred, and non-discriminatory AI deployment in employment and social protection services. Created through a participatory, bottom-up process, these guidelines aim to benefit both employees and citizens.

Since 2021, the German government's data labs have recruited experts in data science and AI from academia, civil society, and the private sector to support the federal administration. These experts help develop and implement AI-driven solutions within government agencies, making data and AI more useful in decision-making and public services. Through exploring and testing new AI applications, the data labs help to modernise administrative processes and promote AI technologies in the public sector

Jointly managed by BeKI, BMF and the Federal Chancellery's Data Lab, KIPITZ is an overarching AI infrastructure supporting LLMs for the federal administration. The platform, built by ITZBund with open-source components, allows cross-departmental testing and deployment of generative AI tools, fostering innovation and digital sovereignty in the public sector.

As a pilot project under BeKI, this marketplace will connect ministries and agencies with AI solutions that meet their needs. It will improve transparency and collaboration by cataloguing AI applications and experiences within government departments. The project is currently being developed with the BMI’s Data Lab, ITZBund, and other ministries, with plans to launch publicly by late 2024.

Coordinated by the AI Advisory Centre (BeKI), AI Guidelines are being developed to ensure a harmonised inter-ministerial approach to the use of AI in public administration. These guidelines promote a value-based approach to AI deployment and offer practical guidance for both employees utilising AI systems and the respective administrative units responsible for providing these systems.

Launched by the BMI, this framework focuses on an opportunity-oriented and responsible approach to AI within the Ministry and its affiliates. It outlines guiding principles, such as human-centred design, transparency, and fairness, and identifies critical factors along the AI value chain that contribute to successful AI use in public administration. Similarly, the Foreign Office (AA) has adopted an AI-Charter.

Germany’s National Data Strategy sets out a comprehensive plan to maximize the potential of data across public, private, and research sectors. It promotes responsible, effective, and sustainable data use while ensuring alignment with EU and national laws. The strategy strengthens data availability, quality, and accessibility to foster a culture of data-driven decision-making throughout society.

The Centre for Artificial Intelligence in Public Health Research (ZKI-PH) at Germany’s Robert Koch Institute supports the use of AI and advanced data methods in public health research. It combines bioinformatics, computational epidemiology, and big data analysis with AI to improve health research outcomes. The centre develops realistic simulations and decision-making tools to address public health challenges.

Germany established uniform and transparent standards for the use of cloud-computing services for processing health data, protecting sensitive health information from unauthorized access, manipulation, or loss. This regulation can positively impact AI use and research by providing a secure and standardised framework for handling sensitive health data, fostering trust and enabling more robust, compliant AI-driven innovations in healthcare.

This funding measure aims to support caregivers as well as caregiving relatives and to improve the self-determination and quality of life of people in need of care through innovative AI applications. Funded projects develop long-term usable data and software repositories as well as AI systems for use in everyday care.

The Health Data Lab (HDL) at the Federal Institute for Drugs and Medical Devices makes pseudonymised claims data from people insured in the statutory health system available for research purposes and other legally defined public interest purposes, including AI development, the HDL makes an important contribution towards better and safer healthcare. With the Act on Health Data Use the Social Security Code – Book V (SGB V) has been amended to explicitly enable the development of AI systems.

In addition to the much-discussed potential in diagnostics, AI-based interactive systems can very effectively support processes in hospitals and comparable healthcare facilities and thus contribute to the improvement of medical, organizational and administrative processes. The aim of the projects in this funding measure is to research and develop AI-based assistance systems that lead to quantifiable and measurable improvements in clinical processes.

Surgical interventions are a core element of multimodal treatment strategies for solid cancers and continue to play a central role in modern oncology. Artificial intelligence is expected to contribute to more personalised and precise approaches to cancer care, supporting clinical decisions before, during, and after surgery. The funding measure seeks to advance the precision of oncological surgery through the development and application of interactive AI technologies.

The GDNG is a key enabler to facilitate the access to healthcare data for secondary use. Its measures include procedural simplifications on re-using health data across multiple states (Länder). It further provides a legal basis for healthcare institutions to re-use their patients‘ data for research, patient safety and quality assurance, and enables statutory health insurance funds to utilize data to improve the quality of care and to support AI applications.

IPCEI-CIS is a significant European initiative involving over 100 companies and research organizations across 12 Member States, with Germany playing a coordinating role. This project aims to establish a decentralized cloud-edge ecosystem that will reduce technological dependencies and foster new data-driven business models. The primary goal of IPCEI-CIS is to create a “Multi-Provider Cloud-Edge Continuum,” which allows for advanced use of data processing resources from cloud-edge environments.

Germany has advanced healthcare interoperability through the Digital Act, which expanded the Coordination Office for Interoperability into a Competence Centre. Supported by the Interop Council and expert working groups, it promotes technical, syntactic, and semantic interoperability, with the Health IT Interoperability Governance Ordinance (GIGV) ensuring a holistic, coordinated approach to developing binding guidelines and standards.

To strengthen the innovative capacity of "Made in Germany" medical technology, the Federal Ministry of Education and Research (BMBF) supports SMEs in their research on innovative medical products, in-vitro diagnostics, and digital medical technology solutions. This funding aims to initiate additional research and development activities, boost medical technology research in Germany, enhance networking between SMEs and partners in science and healthcare.

In many diseases, choosing the appropriate therapy forms the basis for the subsequent course and success of the treatment. The goal of the funding is to optimize patient care through medical technology solutions in the form of innovative decision and support systems. Data-driven approaches should support healthcare providers at all stages of the care process to achieve better healing or a reduction in side effects.

France and Germany launched a joint call for AI projects focused on risk prevention, crisis management, and resilience. One funded project, RenovAIte, develops AI-based software to optimise large-scale renovation of housing and roads. The initiative supports decision-making for engineering firms and local authorities.

The CONTRAILS project is a joint French–German initiative using trusted AI methods and physical models to study and predict condensation trails. Its goal is to assess their climate impact and support strategies to reduce contrail formation. The project is part of a binational call on AI technologies for risk prevention, crisis management, and resilience.