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Governance frameworks, formal policies, and strategic alignment mechanisms.
Also in Oversight & Accountability
Standardized reporting is essential to address the opaque black box issue of AI systems. Organizations should set up standardized processes and templates for informing the development process and product design of AI systems to different stakeholders (e.g., AI governors, users, consumers) [100]. RAI regulations may request such obligations to ensure the transparency and explainability of AI systems.
The Cyberspace Administration of China published transparent disclosure requirements for online service providers.45 The service providers are requested to file with the regulators (i.e., AI governors) for impact assessment when realizing new services. In addition, the online services must inform users when AI is being used to recommend content to them and explain the purposes and design of recommended systems. In the EU’s AI Act,46 the incidents of AI systems are required to be reported and disclosed by AI system providers (i.e., AI technology or solution producers). Helsinki47 and Amsterdam48 released AI registers describing where and how the two cities are using AI, how AI is built, which data and algorithms are used, how the applications impact the citizens’ daily lives, and the development team’s contact information.
Reasoning
Mandated reporting and disclosure obligations requiring organizations to file impact assessments and inform regulators and users about AI systems.
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 (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
