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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
Multi-level co-architecting is required to ensure the seamless integration of different components, including co-architecting AI components and non-AI components and co-architecting of different AI model pipeline components. Multilevel co-architecting allows both system- and model-level requirements to be considered in design decision making.
Compared with traditional software, the architecture of AI systems is more complex due to different levels of integration. On the one hand, AI models are developed by data scientists/engineers via an AI model pipeline. The AI model pipeline usually is composed of a sequence of automatic steps including data collection, data cleaning, feature engineering, model training, and model evaluation. These steps can be viewed as software components for producing AI models from a software architecture perspective. On the other hand, the produced AI models cannot work alone and need to be integrated into software systems that are to be deployed in the real world [82]. The decisions made by the AI model need to be executed as actions via other software components. The architecture of an AI ecosystem consists of three layers: AI software supply chain, AI system, and operation infrastructure. The focus of the AI software supply chain layer is about developing and managing AI and non-AI components [69], including AI model pipeline components, deployment components, co-versioning components, provenance tracking components, and credential management components, among others. The AI system layer comprises AI components that embed AI models and non-AI components that use the outputs of AI components for overall system functionalities [65]. The operation infrastructure layer is mainly about monitoring and feedback components
Reasoning
Design-time architectural choices structuring AI and non-AI component integration across multiple system levels.
Design
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).
Plan and Design
Designing the AI system, defining requirements, and planning development
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
