Despite recent efforts by the Artificial Intelligence (AI) community to move
towards standardised procedures for documenting models, methods, systems or
datasets, there is currently no methodology focused on use cases aligned with
the risk-based approach of the European AI Act (AI Act). In this paper, we
propose a new framework for the documentation of use cases, that we call "use
case cards", based on the use case modelling included in the Unified Markup
Language (UML) standard. Unlike other documentation methodologies, we focus on
the intended purpose and operational use of an AI system. It consists of two
main parts. Firstly, a UML-based template, tailored to allow implicitly
assessing the risk level of the AI system and defining relevant requirements.
Secondly, a supporting UML diagram designed to provide information about the
system-user interactions and relationships. The proposed framework is the
result of a co-design process involving a relevant team of EU policy experts
and scientists. We have validated our proposal with 11 experts with different
backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We
provide the 5 "use case cards" used in the co-design and validation process.
"Use case cards" allows framing and contextualising use cases in an effective
way, and we hope this methodology can be a useful tool for policy makers and
providers for documenting use cases, assessing the risk level, adapting the
different requirements and building a catalogue of existing usages of AI.

本文提出了一种新的用例框架文档方法，名为 “用例卡片”，基于 UML 标准，旨在支持按照欧洲人工智能法案（AI Act）的风险导向方法来制定文档，并提供了关于系统 - 用户交互和关系的支持性 UML 图表，此文档方法能够成为政策制定者和提供者的有用工具。

使用案例卡片：受欧洲 AI 法案启发的用例报告框架

Use case cards: a use case reporting framework inspired by the European  AI Act

Selective classification techniques (also known as reject option) have not
yet been considered in the context of deep neural networks (DNNs). These
techniques can potentially significantly improve DNNs prediction performance by
trading-off coverage. In this paper we propose a method to construct a
selective classifier given a trained neural network. Our method allows a user
to set a desired risk level. At test time, the classifier rejects instances as
needed, to grant the desired risk (with high probability). Empirical results
over CIFAR and ImageNet convincingly demonstrate the viability of our method,
which opens up possibilities to operate DNNs in mission-critical applications.
For example, using our method an unprecedented 2% error in top-5 ImageNet
classification can be guaranteed with probability 99.9%, and almost 60% test
coverage.

本文提出了一种构造选择分类器的方法，可在风险可控的情况下使用深度神经网络进行分类，解决了在深度神经网络中使用拒绝选项技术的问题，有效提高了分类器性能，成功实现了在 ImageNet 上顶部 5 项分类的 2% 错误率，并且测试覆盖率达到近 60%，为深度神经网络在关键应用中的应用提供了可能性。