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Independent audits, third-party reviews, and regulatory compliance verification.
Also in Risk & Assurance
Reasoning
No description or evidence provided; insufficient information to identify focal activity.
RAI governance of APIs
Ethical compliance checking for APIs is needed to detect if any ethics violation exists [50]. A knowledge-driven approach can be adopted to detect ethics issues through ethical knowledge graphs. Ethical knowledge graphs make meaningful entities and concepts, and their relationships in development of AI systems. With the ethical knowledge graph, the rich semantic relationships between entities are explicit and traceable across heterogeneous high-level documents and various AI systems artifacts. Ethical knowledge graphs can be built based on the ethical principles and guidelines (e.g., a privacy knowledge graph based on GDPR [35, 90]) and technical documents (e.g., API documentation) to support the ethical compliance checking for APIs.
2.2.3 Auditing & ComplianceRAI governance via APIs
To avoid harmful dual uses in AI systems [80], developers should carefully design how their AI systems can be directly used and indirectly used (i.e., potential ways their systems can be adapted). Developers must restrict the way AI systems are used and preventing the users from getting around of restrictions by unauthorized reverse engineering or modification to the system design. Rather than fully opening the access to AI systems by allowing AI systems to run locally, developers could provide AI services on the cloud and control the interactions with the AI services via APIs
2.3.2 Access & Security ControlsEthical construction with reuse
Ethical construction with reuse means to develop RAI systems with the use of existing AI artifacts that are compliant with AI ethics principles, such as from an organizational repository or an open source platform. A marketplace can be built up to trade the reusable AI artifacts, including component code, models, and datasets. Blockchain can be adopted to design an immutable and transparent marketplace enabling auction-based trading for AI artifacts and material assets (e.g., cloud resources) [107]. Ethics credentials might be required to be attached to the traded AI artifacts. In addition, tooling support might be needed, such as model migration tool pytorch2keras,86 and glue code for compatibility.87 Low/no code tools can also help to achieve ethical construction with reuse.
2.4.3 Development WorkflowsGovernance 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 (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Unable to classify
Could not be classified to a specific AIRM function
