Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

In recent years, we are witnessing the digital transformation of production lines as part of manufacturers' transition to the fourth industrial revolution (Industry 4.0). Based on Cyber Physical Systems (CPS) and digital technologies like cloud computing, the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), Industry 4.0 is enabling flexible production lines and supporting innovative functionalities like mass customization, predictive maintenance, zero defect manufacturing and digital twins. AI is currently the most disruptive digital enabler of the Industry 4.0 era and enables novel use cases like predictive quality management (Quality 4.0), effective human robot collaboration, agile production, and generative software design. State of the art AI systems in industrial plants operate in rather controlled environments. Nevertheless, AI systems in industrial plants must be safe, trusted, and secure, even when operating in dynamic, unstructured and unpredictable environments.

Trust and transparency are at the forefront of conversations related to artificial intelligence(AI) these days. While we intuitively understand the idea of trusting AI agents, we are still trying to figure out the specific mechanics to translate trust and transparency into programmatic constructs. After all, what does trust means in the context of an AI system? Trust is a foundational building block of human socio-economic dynamics. In software development, during the last few decades, we steadily built mechanisms for asserting trust on specific applications.