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Design-time architectural choices affecting safety, interpretability, and modularity.
Also in Model
The multi-model decision maker employs different models to perform the same task or enable a single decision (e.g., deploying different algorithms for visual perception). It improves the reliability by deploying different models under different contexts (e.g., different geo-location regions) and enabling fault tolerance by cross validating ethical requirements for a single decision [24, 84]. Different consensus protocols could be defined to make the final decision—for example, taking the majority decision. Another strategy is to only accept the same results from the employed models. In addition, the end user or the operator could step in to review the output from the multiple models and make a final decision based on human expertise
Scikit-learn103 is a Python package that supports using multiple learning algorithms to obtain better performance through ensemble learning. The AWS Fraud Detection Using Machine Learning solution trains an unsupervised anomaly detection model in addition to a supervised model, to augment the prediction results.104 IBM Watson Natural Language Understanding uses an ensemble learning framework to include predictions from multiple emotion detection models.
Reasoning
Deploy multiple models with consensus protocols and cross-validation to control execution and improve system reliability.
System patterns
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).
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks
Other
