The Institute for AI Safety and Security provides research and development services in the field of AI-related methods, processes, algorithms and execution environments. The focus is on ensuring safety and security for AI-based solutions in ambitious application classes. “Safety and security by design” is a central aspect in this context, since it directly supports future requirements of safety-critical applications that are based on AI or integrate AI-based components.

Main Research Areas of the Institute for AI Safety and Security are:

• Development processes and methods for safety-critical AI applications
• Robust and reliable approaches to safeguard AI methods and algorithms
• Safety and security in the context of AI-based applications
• Management and use of sensitive data
• Execution environments for AI-based applications
• Innovative computation methods.

The Department for Safety-Critical Data Infrastructures deals with data and information quality as well as the foundations for the development of secure, decentralized data infrastructures. One focus of the department is the development of methods and technologies with a focus on data sovereignty and semantic interoperability for critical and sensitive application scenarios.

Ensuring the high quality of data is crucial for the safety of AI applications. Accordingly, the evaluation of data quality and its preparation for decision-makers is an important step towards safe AI.

The scope of this work is to develop a technical concept and implementation for data quality reports. This includes storage, processing and presentation of the data quality reports, especially for AI applications, research projects, and Gaia-X data spaces. The concept has to consider common ISO standards and the FAIR principles, and also has to supports non-expert users in setting up comprehensive and complete data quality reports.

• Literature research and compilation of the current state of the art for data quality reports - in particular common ISO standards and the FAIR principles - on a technical and content level.
• Development of a technical concept for the storage, processing and presentation of data quality reports for Gaia-X data rooms, AI applications and research projects, considering common ISO standards and the FAIR principles, which also supports non-expert users in entering all standard-relevant data into the report.
• Implementation of the concept in Python as part of a toolbox for data quality evaluation

• Good written and spoken English skills
• Motivation and interest in data
• Structured and independent way of working