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Version control, prototyping, secure development practices, and engineering processes.
Also in Engineering & Development
Ethical construction with reuse means to develop RAI systems with the use of existing AI artifacts that are compliant with AI ethics principles, such as from an organizational repository or an open source platform. A marketplace can be built up to trade the reusable AI artifacts, including component code, models, and datasets. Blockchain can be adopted to design an immutable and transparent marketplace enabling auction-based trading for AI artifacts and material assets (e.g., cloud resources) [107]. Ethics credentials might be required to be attached to the traded AI artifacts. In addition, tooling support might be needed, such as model migration tool pytorch2keras,86 and glue code for compatibility.87 Low/no code tools can also help to achieve ethical construction with reuse.
Building AI systems from scratch can be very complex and time consuming. Very big companies usually have massive AI investments and large volumes of data to compete in the market, whereas smaller companies may only have a couple of data scientists and can hardly keep up with larger companies. To speed up the development and reduce cost, it is highly desirable and valuable to reuse the AI artifacts (i.e., AI components and/or AI pipeline artifacts) across different applications. However, there might be ethical quality issues with the reused AI artifacts, which requires further assurance mechanisms
Reasoning
Organizational development workflow adopting reusable, ethics-compliant AI artifacts through marketplace platforms and tooling support.
Implementation
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).
Build and Use Model
Training, fine-tuning, and integrating the AI model
Developer
Entity that creates, trains, or modifies the AI system
Govern
Policies, processes, and accountability structures for AI risk management
Primary
7 AI System Safety, Failures & Limitations