Data Scientist Specialized in Assuring Safety

With the "Data Scientist Specialized in Assuring Safety" certificate, we certify practical knowledge of the state of the art at the interface between functional safety and artificial intelligence, including relevant standards and standardization initiatives.

A certified "Data Scientist Specialized in Assuring Safety"

• has knowledge of the risk and innovation potential of AI applications in a safety-critical environment
• has an overview of the fundamentals of safety engineering,
• knows relevant AI principles from a safety perspective,
• can classify the benefits and binding nature of safety and AI standards,
• knows a selection of possible strategies and measures for safe AI
• and can apply assurance cases as a possible basis of argumentation for AI-related safety proofs.

Target group

Safety engineers, data scientists and other people who are looking for a deeper insight into the challenges and possibilities of using AI in safety-critical systems, for example people from testing organizations, managers and quality and project managers.

• Motivation for AI in safety-critical systems: Applications, risks, correlations: trustworthiness and safety
• Safety engineering basics: important terminology, tasks, basic procedure, safety requirements and classic measures, integrity level
• Safety-related AI basics: important terminology, model types, processes, basic approach to machine learning, deep neural networks and specific challenges for safety
• Relevant standards and norms: Regulation, importance of standards, possibilities of classification, applicability of safety standards, overview
• Safety measures for AI: classification, structuring by means of life cycle
• Specification measures: overview of measures for AI components
• Design measures: overview of measures for AI components
• Measures during testing: Overview of measures for AI components
• Measures during analysis: Overview of measures for AI components
• Measures in the data lifecycle: overview of data quality measures
• Basics of assurance cases: Possibility of security argumentation, notation, general argumentation strategies
• Assurance cases for AI components: Argumentation patterns for AI components, consolidation by classifying the measures learned