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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
Data requirements need to be listed explicitly and specified throughout the data lifecycle (i.e., collection, cleaning, preparation, validation, analysis, and termination), taking into account ethical principles and involved stakeholders (i.e., data providers, data engineers, data scientists, data consumers, data auditors). Data requirements can be managed through data requirements specification. The specification could include detailed requirements for each phase in the data lifecycle, such as data collection requirements including data sources and collection methods. Google has created a template for dataset requirements specification
The quality of an AI model is largely dependent on the quality of the data used to train or evaluate. The lifecycle of data consists of several phases, including data collection, cleaning, preparation, validation, analysis, and termination. Unfortunately, the scope of data requirements [104, 118] often focuses on the data analysis phase and largely neglects the other key phases in the data lifecycle. This may lead to downstream ethical concerns such as AI model reliability, accountability, and fairness. AI systems can hardly be trusted when the data lifecycle is poorly managed.
Reasoning
Establishes explicit data requirements throughout collection, validation, preparation phases to ensure training data quality and composition.
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).
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
