Fairness is one of the most commonly identified ethical principles in existing AI guidelines, and the development of fair AI-enabled systems is required by new and emerging AI regulation. But most approaches to addressing the fairness of AI-enabled systems are limited in scope in two significant ways: their substantive content focuses on statistical measures of fairness, and they do not emphasize the need to identify and address fairness considerations across the whole AI lifecycle. Our contribution is to present an assurance framework and tool that can enable a practical and transparent method for widening the scope of fairness considerations across the AI lifecycle and move the discussion beyond mere statistical notions of fairness to consider a richer analysis in a practical and context-dependent manner. To illustrate this approach, we first describe and then apply the framework of Trustworthy and Ethical Assurance (TEA) to an AI-enabled clinical diagnostic support system (CDSS) whose purpose is to help clinicians predict the risk of developing hypertension in patients with Type 2 diabetes, a context in which several fairness considerations arise (e.g., discrimination against patient subgroups). This is supplemented by an open-source tool and a fairness considerations map to help facilitate reasoning about the fairness of AI-enabled systems in a participatory way. In short, by using a shared framework for identifying, documenting and justifying fairness considerations, and then using this deliberative exercise to structure an assurance case, research on AI fairness becomes reusable and generalizable for others in the ethical AI community and for sharing best practices for achieving fairness and equity in digital health and healthcare in particular.

翻译：公平性是现有AI指南中最常被识别的伦理原则之一，新兴的AI法规也要求开发公平的AI系统。但当前解决AI系统公平性的方法在两大关键维度上存在局限：其核心内容聚焦于统计意义上的公平性度量，且未能强调在全AI生命周期中识别和处理公平性问题的必要性。本文的贡献在于提出一个保障框架及配套工具，通过实用且透明的方法拓展AI生命周期中的公平性考量范围，推动讨论超越单纯的统计公平概念，以实践导向和情境依赖的方式实现更丰富的分析。为阐释该方法，我们首先描述并应用可信与伦理保障框架至一个AI临床诊断支持系统案例中——该系统旨在辅助临床医生预测2型糖尿病患者发展高血压的风险，该场景中存在多重公平性问题。该框架辅以开源工具和公平性考量图谱，以参与式方法促进对AI系统公平性的论证。简言之，通过采用共享框架来识别、记录和论证公平性考量，并以此协商过程构建保障案例，AI公平性研究得以在伦理AI社区内实现可复用性与可推广性，特别为数字健康与医疗领域实现公平公正的最佳实践共享提供支撑。