Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (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. In addition, 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 RAI from a system perspective, in this article, we present an RAI Pattern Catalogue based on the results of a multivocal literature review. 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 RAI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement RAI.

负责任的人工智能（RAI）被广泛认为是我们这个时代最大的科学挑战之一，也是提高人工智能（AI）采用的关键。最近，多个人工智能伦理原则框架相继发布。然而，如果没有关于最佳实践的进一步指导，实践者除了老生常谈之外什么也没有。此外，在算法层面而不是系统层面做出了重大努力，主要集中在符合数学的道德原则的子集，例如公平性。然而，道德问题可能出现在开发生命周期的任何阶段，涉及人工智能算法和模型之外的系统的许多人工智能和非人工智能组件。为了从系统角度实施 RAI，在本文中，我们基于多语言文献综述的结果提出了 RAI 模式目录。我们不停留在原理或算法层面，而是关注人工智能系统利益相关者在实践中可以采取的模式，以确保开发的人工智能系统在整个治理和工程生命周期中负责。 RAI 模式目录将模式分为三组：多级治理模式、可信流程模式和 RAI-by-design 产品模式。这些模式为利益相关者实施 RAI 提供了系统且可操作的指导。