  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.
