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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
Explainable Artificial Intelligence (XAI) can be viewed as a human-AI interaction problem and achieved by effective human-centered interface design. Checklists (e.g., a question bank) are often used to help design the explainable user interfaces [62, 66, 67] and understand the user needs, choices of XAI techniques, and XAI design factors [67]. For example, the checklist questions could consider the following aspects [66] for different stakeholders: input, output, how, performance (can be extended to ethical performance), why and why not, what if, and so on.
The design of conversational interfaces can be experimented via a Wizard of Oz study [56] in which users interact with a system that they believe to be autonomous but is actually being operated by a hidden human, called the Wizard. The conversation data is collected and analyzed to understand requirements for a self-explanatory conversational interface. There can be several ways to increase human trust in AI systems through a human-centered user interface, including anthropomorphism [72] and proactive informing (e.g., capability/limitation of AI systems, ethics credentials, explanations of decisions/behaviors, potential outcomes, data use information).
Reasoning
Establishes design standards and interface guidelines for explainable AI systems through checklists and human-centered design principles.
Design
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).
Plan and Design
Designing the AI system, defining requirements, and planning development
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks
