用于基于论证的 AI 公平性保证的合理证据收集
Justified Evidence Collection for Argument-based AI Fairness Assurance
AI总结：
本文提出系统工程驱动且有工具支持的两阶段动态论证框架，通过公平性治理定义主张并持续收集模型、数据等证据，以保障 AI 公平性。
摘要：
人们普遍认识到，确保公平的 AI 系统是一项复杂的社会技术挑战，需要在系统生命周期的所有阶段（从定义需求到模型部署和停用）进行审慎考量和持续监督。动态的基于论证的保证案例通过呈现由证据支持的结构化论证，已成为评估和缓解 AI 赋能系统开发中安全风险与危害的系统化方法，并已被扩展用于处理公平性、可解释性等更广泛的规范性目标。本文介绍了一个由系统工程驱动、并有软件工具支持的框架，分两个阶段将基于论证的保证动态方法落地。第一阶段在需求规划期间，由多学科、多利益相关方团队通过开展全面的公平性治理流程，定义待确立（并需证据支持）的目标和主张。第二阶段，持续监控界面从模型、数据和用例文档等现有工件（例如自动化测试产生的指标）中收集证据，以动态支持这些论证。该框架的有效性通过金融领域的一个说明性案例研究得到展示，重点是支持与公平性相关的论证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
