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Laws, legal frameworks, and binding policy instruments governing AI development and use.
Also in Legal & Regulatory
To enable the trial of the innovative AI products in the market, a regulatory sandbox can be designed to allow testing the innovative AI products in the real world under relaxed regulatory requirements but with appropriate safeguards in place on a time-limited and small-scale basis [98].
An AI Regulatory Sandbox9 is introduced in the EU’s AI Act proposal submitted in 2021. The UK Information Commissioner’s Office advised a Regulatory Sandbox 10 for utilizing personal data. The Australian Government released the Enhanced Regulatory Sandbox11 for innovative financial services. AI products can enter the market under more flexible regulatory requirements in a faster pace and be tested in the real-world market to ensure that they are designed ethically. However, it might incur extra cost to apply for a regulatory sandbox. In addition, the AI products might not work well with large-scale deployment in different contexts.
Reasoning
Regulatory sandbox established by state authority to govern AI product testing under relaxed but safeguarded requirements.
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).
Deploy
Releasing the AI system into a production environment
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