为AI决策提供可审计、防篡改的全过程追踪流程，保障责任可追溯。
A Workflow for Full Traceability of AI Decisions
- 强制记录训练与推理中每个组件的输入输出，形成完整决策链。
- 通过可信计算技术实现不可伪造的决策溯源，支持法律追责。
- 在毒菇识别应用中验证可行性，适合高风险场景的AI系统部署。
越来越多高风险决策由依赖脆弱人工智能技术的自动化系统做出或辅助，存在侵犯个人福祉或基本人权的风险。当前AI系统对决策过程的文档化严重不足，难以追溯决策依据，阻碍责任链条的重建。尤其在涉及法律违规时，缺乏可被法庭采信的证据。本文提出一种激进但实用的方法，强制记录训练与推理过程中所有组件的输入输出，首次实现可防篡改、可验证、完整的AI决策追踪工作流。通过将DBOM概念扩展为实际可用的流程，并结合可信计算技术，我们以一款用于区分毒蘑菇与可食用蘑菇的应用为例，演示了该流程的运行机制，展示了其在高风险决策支持中的潜力。
原文摘要 · Abstract (English)
An ever increasing number of high-stake decisions are made or assisted by automated systems employing brittle artificial intelligence technology. There is a substantial risk that some of these decision induce harm to people, by infringing their well-being or their fundamental human rights. The state-of-the-art in AI systems makes little effort with respect to appropriate documentation of the decision process. This obstructs the ability to trace what went into a decision, which in turn is a prerequisite to any attempt of reconstructing a responsibility chain. Specifically, such traceability is linked to a documentation that will stand up in court when determining the cause of some AI-based decision that inadvertently or intentionally violates the law. This paper takes a radical, yet practical, approach to this problem, by enforcing the documentation of each and every component that goes into the training or inference of an automated decision. As such, it presents the first running workflow supporting the generation of tamper-proof, verifiable and exhaustive traces of AI decisions. In doing so, we expand the DBOM concept into an effective running workflow leveraging confidential computing technology. We demonstrate the inner workings of the workflow in the development of an app to tell poisonous and edible mushrooms apart, meant as a playful example of high-stake decision support.
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