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Responsible AI is widely considered as one of the greatest scientificchallenges of our time and is key to increase the adoption of AI. Recently, anumber of AI ethics principles frameworks have been published. However, withoutfurther guidance on best practices, practitioners are left with nothing muchbeyond truisms. Also, significant efforts have been placed at algorithm-levelrather than system-level, mainly focusing on a subset of mathematics-amenableethical principles, such as fairness. Nevertheless, ethical issues can arise atany step of the development lifecycle, cutting across many AI and non-AIcomponents of systems beyond AI algorithms and models. To operationalizeresponsible AI from a system perspective, in this paper, we present aResponsible AI Pattern Catalogue based on the results of a MultivocalLiterature Review (MLR). Rather than staying at the principle or algorithmlevel, we focus on patterns that AI system stakeholders can undertake inpractice to ensure that the developed AI systems are responsible throughout theentire governance and engineering lifecycle. The Responsible AI PatternCatalogue classifies the patterns into three groups: multi-level governancepatterns, trustworthy process patterns, and responsible-AI-by-design productpatterns. These patterns provide systematic and actionable guidance forstakeholders to implement responsible AI.

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering