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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
This section provides system-level guidance on how to design the architecture of RAI systems. We present a collection of product patterns (i.e., design patterns) for building RAI-by-design into AI systems (Figure 6). Broadly, an AI system is composed of three layers: (1) the supply chain layer that generates the software components which compose the AI system, (2) the system layer which is the deployed AI system, and (3) the operation infrastructure layer that provides auxiliary functions to the AI system. Figure 7 presents the identified products patterns for each of the three layers. Those product patterns can be embedded into the AI ecosystems as product features. Figure 7 illustrates a state diagram of a provisioned AI system and highlights the patterns associating with relevant states or transitions, which show when the product patterns could take effect.
Reasoning
Design patterns and architectural guidance for building responsible AI systems across supply chain, system, and operation layers.
Governance Patterns
The governance for RAI systems can be defined as the structures and processes that are employed to ensure that the development and use of AI systems meet AI ethics principles. According to the structure of Shneiderman [104], governance can be built at three levels: industry level, organization level, and team level.
2.1 Oversight & AccountabilityGovernance Patterns > Industry-level governance patterns
3.1 Legal & RegulatoryGovernance Patterns > Organization-level governance patterns
2.1 Oversight & AccountabilityGovernance Patterns > Team-level governance patterns
2.1.2 Roles & AccountabilityProcess Patterns
The process patterns are reusable methods and best practices that can be used by the development team during the development process.
2.4.2 Design StandardsProcess Patterns > Requirement Engineering
2.4 Engineering & DevelopmentResponsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Lu, Qinghua; Zhu, Liming; Xu, Xiwei; Whittle, Jon; Zowghi, Didar; Jacquet, Aurelie (2024)
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. © 2024 Copyright held by the owner/author(s).
Other (multiple stages)
Applies across multiple lifecycle stages
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
