This page is still being polished. If you have thoughts, please share them via the feedback form.
Data on this page is preliminary and may change. Please do not share or cite these figures publicly.
Structured analysis to identify, characterize, and prioritize potential harms and risks.
Also in Risk & Assurance
The ethical risk assessment framework can be built with guided extension points for different contexts (e.g., culture context). The risk mitigation can be designed from three aspects: reducing frequency occurrence, consequence size, and consequence response. Extensible, adaptive, and dynamic risk assessment can effectively ensure that an AI system adheres to AI ethics principles throughout the whole lifecycle, but it might be hard to measure some of the ethical principles, such as humancentered values.
The current risk-based approach to ethical principles is often a done-once-and-forget type of algorithm-level risk assessment [45, 73, 99, 104, 130] and mitigation for a subset of ethical principles (e.g., privacy or fairness88) at a particular development step (e.g., Canada’s Algorithmic Impact Assessment Tool89), which is not sufficient for the highly uncertain and continual learning AI systems. In addition, the context of AI systems varies with the application domains, organizations, culture, and regions. It is essential to perform continuous risk assessment and mitigation of RAI systems
Reasoning
Structured framework identifies and prioritizes ethical risks continuously across AI system lifecycle contexts.
Operation
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 (multiple stages)
Applies across multiple lifecycle stages
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Measure
Quantifying, testing, and monitoring identified AI risks
