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Laws, legal frameworks, and binding policy instruments governing AI development and use.
Also in Legal & Regulatory
RAI Regulation. Laws already apply to AI systems; however, the processes/requirements to ensure compliance are not always certain, and also some regulations may need to be updated (e.g., administrative law). There is an urgent need for clear guidance to ensure that AI systems are developed and used responsibly in compliance with existing and upcoming laws (e.g., discrimination law). RAI regulations are developed by governments in their jurisdiction to enable the trustworthy development of AI systems by industry
Organizations will be required to ensure that they comply with the requirements of the EU AI Act when the applications fall into the high-risk category.6 In the United States, the Algorithmic Accountability Act of 20227 was introduced in the Senate and House of Representatives, and an AI Bill of Rights8 is under development by the White House Office of Science and Technology Policy. The aim of RAI regulations is to prevent illegal or negligent, malicious use of AI systems. However, there are many regulations in developments in each jurisdictions, which may cause an interoperability challenge for organizations. In addition, it usually takes a long time to enact AI regulations due to the lengthy consultation and approval process.
Reasoning
Government-developed laws establishing binding requirements for responsible AI development and compliance across jurisdictions.
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
