Fostering Austria's Innovative strength and Research excellence in Artificial Intelligence - Lead project AI Mission Austria funding initiative

The barriers to the implementation of AI in development and management are complex and arise from a) technical reasons (e.g. intrinsic technical risks of current machine learning such as data shifts in a non-stationary environment), b) technical and management challenges (e.g. the need for a highly skilled workforce, high initial costs and project risks at the project management level) and c) socio-technical application-related factors (e.g. the need for risk awareness in the application of AI). the need for highly skilled labour, high initial costs and project risks at the project management level) and c) socio-technical application-related factors (e.g. the need for risk awareness in the application of AI, including human factors such as cognitive biases in AI-based decision making). In this context, we consider the identification, monitoring and, where possible, anticipation of risks at all levels of system development and application to be a key factor.

FAIR-AI follows a methodology to disentangle these types of risks. Instead of claiming a general solution to this problem, our approach follows a bottom-up strategy by selecting typical pitfalls in a specific development and application context to create a collection of instructive, self-contained use cases implemented in research modules to illustrate the intrinsic risks. We go beyond the state of the art and explore possibilities of risk disentanglement, prediction and their integration into a recommender system capable of providing active support and guidance.

FAIR-AI is regarded as a major flagship project in the field of artificial intelligence. It aims to simplify the research and development of AI systems in Austria in order to support the Austrian research landscape and economic application development. This is to be achieved through various measures:

• Development of solutions in the areas of sustainability, circular economy and climate neutrality
• Risk minimisation in AI development through new tools developed as part of the project and specially tailored training opportunities
• Supporting the Austrian innovation system in the field of AI
• Optimising R&D processes
• Networking stakeholders with the aim of increasing the competitiveness of the Austrian AI landscape and pooling research activities and results

FAIR AI Workshop Modules: Strategic Expertise for the Use of AI

As part of the project, the University of Applied Sciences Burgenland has developed four modules that provide the in-depth expertise needed to use AI responsibly and in compliance with legal requirements. The workshops are aimed at managers and experts in companies who want not only to understand AI as a technology but also to integrate it safely into business processes.

1. AI Technology & Concepts

Core pillars: Introduction to Machine Learning (ML) and Large Language Models (LLMs)

Content: Distinguishing between supervised, unsupervised, and reinforcement learning; understanding design decisions such as feature selection and model selection

Learning objective: Participants understand why data quality is essential, how to interpret evaluation metrics, and where the physical and logical limitations of current AI systems lie.

2. AI Management

Core pillars: Responsible AI use at the strategic level

Content: Concepts such as “human in the loop” and “human on the loop”; implementation of AI governance structures and control mechanisms to minimise risks

Learning objective: The ability to navigate the European regulatory framework with legal certainty and incorporate compliance requirements into project planning at an early stage.

3. AI Compliance

Core pillars: The EU AI Act, the GDPR, and liability issues

Content: The EU AI Act, the GDPR, and related liability issues. This includes the classification of AI systems by risk category, transparency obligations, and the protection of intellectual property (IP) rights. It also covers IP-related aspects ranging from AI training data to AI-generated content, as well as unfair competition law consequences arising from the infringement of IP rights.

Learning objective: The ability to navigate the European regulatory framework with legal certainty and incorporate compliance requirements into project planning at an early stage

4. AI Fairness

Core pillars: Bias detection and inclusive AI

Content: Definitions of fairness, including demographic parity and equalised odds; the causes and different levels of AI bias in data and algorithms; and technical methods such as explainable AI (XAI)

Learning objective: The ability to identify discrimination risks in sensitive areas, such as recruitment and credit assessment, and apply best practices for transparent, interdisciplinary AI development

The programme bridges the gap between technological innovation and regulatory certainty. It enables companies not only to implement AI projects efficiently but also to make them future-proof and trustworthy.