  Responsible AI is widely considered as one of the greatest scientific
challenges of our time and is key to increase the adoption of AI. Recently, a
number of AI ethics principles frameworks have been published. However, without
further guidance on best practices, practitioners are left with nothing much
beyond truisms. Also, significant efforts have been placed at algorithm-level
rather than system-level, mainly focusing on a subset of mathematics-amenable
ethical principles, such as fairness. Nevertheless, ethical issues can arise at
any step of the development lifecycle, cutting across many AI and non-AI
components of systems beyond AI algorithms and models. To operationalize
responsible AI from a system perspective, in this paper, we present a
Responsible AI Pattern Catalogue based on the results of a Multivocal
Literature Review (MLR). Rather than staying at the principle or algorithm
level, we focus on patterns that AI system stakeholders can undertake in
practice to ensure that the developed AI systems are responsible throughout the
entire governance and engineering lifecycle. The Responsible AI Pattern
Catalogue classifies the patterns into three groups: multi-level governance
patterns, trustworthy process patterns, and responsible-AI-by-design product
patterns. These patterns provide systematic and actionable guidance for
stakeholders to implement responsible AI.
