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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
The development of AI systems needs to adhere to AI ethics principles which are generally abstract and domain agnostic. Ethical requirements need to be derived from the AI ethics principles to fit into a specific domain and system context [16, 49, 93, 118, 128]. Every ethical requirement specified in a requirements specification document should be put into a verifiable form (i.e., with acceptance criteria). This means that a person or machine can later check that the AI system meets the ethical requirements that are derived from AI ethics principles and grounded in users’ needs. Vague or unverifiable statements should be avoided [110]. If there is no way to determine whether the AI system meets a particular ethical requirement, then this ethical requirement should be revised or removed.
Ethical risk can be reduced via considering ethical requirements from the beginning of the development process and explicitly verifying ethical requirements. Some ethical principles/requirements may not be easily quantitatively validated [128], such as human-centered values. There may be tradeoffs between some ethical principles or requirements. The current practice to deal with the tradeoffs is usually the developers following one principle while overwriting the others rather than building balanced tradeoffs through patterns.
Reasoning
Establish verifiable acceptance criteria to document and demonstrate AI systems meet ethical requirement standards.
Requirement Engineering
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
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
