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Structured analysis to identify, characterize, and prioritize potential harms and risks.
Also in Risk & Assurance
Stakeholder engagement allows AI systems to better reflect their stakeholders’ needs and expectations [17, 104, 129, 131]. There are various manners to engage stakeholders, such as interviews, online and offline meetings, project planning/review,participatory design workshops, and crowd sourcing. Stakeholders may help the project team identify potential ethical risks before they become threats, but there maybe conflicting opinions from different stakeholders.
The Association for Project Management published 10 stakeholder engagement principles.68 The Australian Public Service Commission released stakeholder engagement guidelines.69 Deloitte published a report on stakeholder engagement
Reasoning
Stakeholder engagement identifies potential ethical risks through structured group analysis before deployment.
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
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failure