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.
Version control, prototyping, secure development practices, and engineering processes.
Also in Engineering & Development
Multi-level co-versioning provides end-to-end traceability and accountability throughout the whole lifecycle of AI systems, but the collection and documentation of co-versioning information incur additional development cost. There have been many version control tools in industry focusing on supply chain level co-versioning, such as MLflow Model Registry on Databricks90 and the Amazon provenance tool,91 and Data Version Control (DVC).9
AI systems involve two levels of relationships and dependencies across various AI artifacts, including the supply chain level and the system level. At the system level, there are multiple versions of AI components and non-AI components. At the supply chain level, there are different versions of data, model, code, and configuration, which are used to produce different versions of AI components [65]. At the system level, the AI components that embed AI models are integrated into AI systems and interact with non-AI components. However, the retraining of AI models introduces new versions of data, code, and configuration parameters. If federated learning is adopted, for each round of training, a global model is ensembled based on local models sent from participating clients [70]. It is important to capture all of these dependencies during the development process
Reasoning
Version control infrastructure tracks dependencies across development lifecycle artifacts and components.
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
Developer
Entity that creates, trains, or modifies the AI system
Govern
Policies, processes, and accountability structures for AI risk management
