Mitigation of AI-inherent risks in AI-supported industrial development and scientific research

Abstract

This presentation gives an overview over measures that can be taken to mitigate artificial intelligence (AI)-inherent risks in scientific and industrial research and development. Parts of the presentation are based on results of the research project progressivKI on AI-support for electronic design automation (EDA), in which an EDA-service platform has been developed and provided for industrial applications. Further contributions are related to machine learning (ML) and AI applications in Life Sciences and Biology. Particularly, questions on the quality of AI-supported scientific research are discussed: In plant biology, artificial intelligence is gradually establishing itself as a tool of first choice for large-scale studies on both the genomic and the phenotypic level. In addition, measures to protect data exchange via the European cloud GAIA-X, which is used in some of the projects mentioned above, are discussed.

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