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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
Design modeling methods can be extended and used to support the modeling of AI components and the ethical aspects, including using UML to describe the architecture of AI systems and represent their ethical aspects [114], designing formal models taking into account human values [36], using ontologies to model the AI system artifacts for accountability [10, 85], establishing RAI knowledge bases for making design decisions considering ethical concerns [101], and using logic programming to implement ethical principles
UML is an option to describe the AI systems and represent their ethical aspects [114]. The UML extension could be a declarative graphic notation for AI system architecture. Additional stereotypes/metamodel elements can be added for RAI-by-design reference architecture (e.g., to describe AI pipeline components). Use case diagrams can help define the stakeholders and explain the functions they use, which are valuable for achieving accountability. State diagrams are useful to analyze the system states and identify the states that may cause ethical failures. Design patterns like the AI mode switcher can take effect to change the state of an AI system to a more human-controlled state. Sequence diagrams describe the human-AI interactions to ensure all the required explanations are provided. Using design modeling methods is helpful to capture and analyze ethical principles in design. One disadvantage when using modeling languages is the time to create and manage the models. In addition, the modeling languages do not scale up for large and complex systems.
Reasoning
Establishes design standards for modeling AI architecture with ethical considerations using UML, ontologies, and formal methods.
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
