在实践中可解释人工智能意味着什么？来自长期临床案例研究的评估要求
What Does Explainable AI Mean in Practice? Evaluative Requirements from a Longitudinal Clinical Case Study
AI总结：
本文通过长期临床案例研究，探讨了在专家领域中构建可信AI系统所需的评估需求，发现医生更信任能与自身评估对比的简单预测图。
摘要：
本文报告了一项案例研究，探讨了在开发用于预测婴儿脑瘫（CP）风险的AI系统过程中如何提出可解释性需求。在18个月的时间里，我们跟踪了一个开发团队和医院临床医生，他们试图设计能够使AI系统可信的解释。与假设用户需要详细解释AI系统内部工作原理相反，我们的发现表明，当系统使医生能够将预测结果与自己的评估进行比较时，他们会信任它。我们的发现展示了简单的预测图通过支持医生现有的决策实践而证明了其有效性。基于需求工程和可解释人工智能的概念，我们使用评估人工智能的理论视角，引入了评估需求的概念：允许用户审查AI输出并与自身评估进行比较的系统需求。我们的研究证明，此类需求最好通过广为人知的迭代原型设计和观察方法来发现，使其成为在专家领域构建可信AI系统的关键。
英文摘要：
This paper reports a case study on how explainability requirements were elicited during the development of an AI system for predicting cerebral palsy (CP) risk in infants. Over 18 months, we followed a development team and hospital clinicians as they sought to design explanations that would make the AI system trustworthy. Contrary to the assumption that users need detailed explanations of the inner workings of AI systems, our findings show that clinicians trusted it when it enabled them to evaluate predictions against their own assessments. Our findings show how a simple prediction graph proved effective by supporting clinicians' existing decision-making practices. Drawing on concepts from both Requirements Engineering and Explainable AI, we use the theoretical lens of Evaluative AI to introduce the notion of Evaluative Requirements: system requirements that allow users to scrutinize AI outputs and compare them with their own assessments. Our study demonstrates that such requirements are best discovered through the well-known methods of iterative prototyping and observation, making them essential for building trustworthy AI systems in expert domains.
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。
