用动态论证框架持续收集证据，保障AI系统公平性。
Justified Evidence Collection for Argument-based AI Fairness Assurance
- 分两阶段构建公平性论证：需求期定义目标，运行期自动采集证据。
- 通过金融案例验证，实现对公平性主张的持续证据支持。
- 适合需要合规审计与持续公平性监控的AI开发团队。
确保AI系统的公平性是一项复杂的社技术挑战，需在系统全生命周期中持续审议与监督。动态论证保证案例通过结构化论据与证据支持，已成为评估和缓解AI系统安全风险的有效方法，并已拓展至公平性、可解释性等规范目标。本文提出一种基于系统工程的框架，结合软件工具，分两阶段实施动态论证保证：第一阶段，在需求规划期，多学科、多利益相关方团队通过全面的公平治理过程定义需建立并证明的目标与主张；第二阶段，通过持续监控界面从现有文档（如模型、数据、用例文档）及自动化测试指标中动态收集证据，支撑论证。该框架在金融领域的示范案例中得到验证，重点支持公平性相关论据。
原文摘要 · Abstract (English)
It is well recognised that ensuring fair AI systems is a complex sociotechnical challenge, which requires careful deliberation and continuous oversight across all stages of a system's lifecycle, from defining requirements to model deployment and deprovisioning. Dynamic argument-based assurance cases, which present structured arguments supported by evidence, have emerged as a systematic approach to evaluating and mitigating safety risks and hazards in AI-enabled system development and have also been extended to deal with broader normative goals such as fairness and explainability. This paper introduces a systems-engineering-driven framework, supported by software tooling, to operationalise a dynamic approach to argument-based assurance in two stages. In the first stage, during the requirements planning phase, a multi-disciplinary and multi-stakeholder team define goals and claims to be established (and evidenced) by conducting a comprehensive fairness governance process. In the second stage, a continuous monitoring interface gathers evidence from existing artefacts (e.g. metrics from automated tests), such as model, data, and use case documentation, to support these arguments dynamically. The framework's effectiveness is demonstrated through an illustrative case study in finance, with a focus on supporting fairness-related arguments.
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