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Shared theoretical frameworks, research tools, and foundational resources for the field.
Also in Shared Infrastructure
An ethical knowledge base, such as a knowledge graph, makes meaningful entities and concepts, and their relationships in design, implementation, deployment, and operation of AI systems [32, 85, 101]. With the ethical knowledge base, the rich semantic relationships between entities are explicit and traceable across heterogeneous high-level documents on one hand and different artifacts across the AI system lifecycle on the other hand. Thus, ethical requirements of the AI system can be systematically accessed and analyzed using the ethical knowledge base
Awesome AI Guidelines112 aims to provide a mapping between ecosystem of guidelines, principles, codes of ethics, standards, and regulation around AI. The RAI community portal113 is provided by AI Global, which is an evolving repository of reports, standards, models, government policies, datasets, and open source software to inform and support RAI development. Responsible AI Knowledge-base114 is a knowledge base of different areas using and developing AI in a responsible way.
Reasoning
Shared research resources creating ethical knowledge graphs and repositories to inform responsible AI development across ecosystem.
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
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Map
Identifying and documenting AI risks, contexts, and impacts
Primary
6.5 Governance failure