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

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

The FAIR-AI project addresses the research gap that arises from dealing with societal risks in the application of AI. In particular, the requirements of the upcoming European AI law and the obstacles to its implementation in the daily development and management of AI-based projects and its AI law-compliant application are at the centre of interest.

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 risk types. 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, which are 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 educational 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