Guideline for Trustworthy Artificial Intelligence -- AI Assessment
  Catalog
Maximilian Poretschkin Anna Schmitz Maram Akila Linara Adilova Daniel Becker Armin B. Cremers Dirk Hecker Sebastian Houben Michael Mock Julia Rosenzweig Joachim Sicking Elena Schulz Angelika Voss Stefan Wrobel
Artificial Intelligence (AI) has made impressive progress in recent years and
represents a key technology that has a crucial impact on the economy and
society. However, it is clear that AI and business models based on it can only
reach their full potential if AI applications are developed according to high
quality standards and are effectively protected against new AI risks. For
instance, AI bears the risk of unfair treatment of individuals when processing
personal data e.g., to support credit lending or staff recruitment decisions.
The emergence of these new risks is closely linked to the fact that the
behavior of AI applications, particularly those based on Machine Learning (ML),
is essentially learned from large volumes of data and is not predetermined by
fixed programmed rules.
  Thus, the issue of the trustworthiness of AI applications is crucial and is
the subject of numerous major publications by stakeholders in politics,
business and society. In addition, there is mutual agreement that the
requirements for trustworthy AI, which are often described in an abstract way,
must now be made clear and tangible. One challenge to overcome here relates to
the fact that the specific quality criteria for an AI application depend
heavily on the application context and possible measures to fulfill them in
turn depend heavily on the AI technology used. Lastly, practical assessment
procedures are needed to evaluate whether specific AI applications have been
developed according to adequate quality standards. This AI assessment catalog
addresses exactly this point and is intended for two target groups: Firstly, it
provides developers with a guideline for systematically making their AI
applications trustworthy. Secondly, it guides assessors and auditors on how to
examine AI applications for trustworthiness in a structured way.