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

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering QINGHUA LU, LIMING ZHU, XIWEI XU, JON WHITTLE, DIDAR ZOWGHI, and AURELIE JACQUET, Data61, CSIRO, Australia 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, practition- ers 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.

Authors: Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Didar Zowghi, Aurelie Jacquet

Published in: ACM Computing Surveys

Publication date: 2024-07-31

Read the paper: https://doi.org/10.1145/3626234

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