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.

翻译：负责任AI被广泛认为是我们时代最重大的科学挑战之一，也是提升AI应用普及的关键。近年来，众多AI伦理原则框架相继发布。然而，缺乏进一步的最佳实践指导，从业者只能停留在空洞的口号层面。此外，现有研究主要侧重于算法层面而非系统层面，尤其聚焦于公平性等易于数学化的伦理原则。但伦理问题可能出现在AI开发全生命周期的任何环节，涉及AI算法和模型之外的诸多AI及非AI系统组件。为从系统视角实现负责任AI，本文基于多语音文献综述（MLR）成果，提出了一份负责任AI模式目录。我们不拘泥于原则或算法层面，而是聚焦于AI系统利益相关者可在实践中采用的模式，确保所开发AI系统在整个治理与工程生命周期中均保持负责任性。该目录将模式分为三类：多层级治理模式、可信流程模式及负责任AI自设计产品模式。这些模式为利益相关者实施负责任AI提供了系统化且可操作的指导。