Current ethical debates on the use of artificial intelligence (AI) in health care treat AI as a product of technology in three ways: First, by assessing risks and potential benefits of currently developed AI-enabled products with ethical checklists; second, by proposing ex ante lists of ethical values seen as relevant for the design and development of assisting technology, and third, by promoting AI technology to use moral reasoning as part of the automation process. Subsequently, we propose a fourth approach to AI, namely as a methodological tool to assist ethical reflection. We provide a concept of an AI-simulation informed by three separate elements: 1) stochastic human behavior models based on behavioral data for simulating realistic settings, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components that aid in understanding the impact of changes in these variables. The potential of this approach is to inform an interdisciplinary field about anticipated ethical challenges or ethical trade-offs in concrete settings and, hence, to spark a re-evaluation of design and implementation plans. This may be particularly useful for applications that deal with extremely complex values and behavior or with limitations on the communication resources of affected persons (e.g., persons with dementia care or for care of persons with cognitive impairment). Simulation does not replace ethical reflection but does allow for detailed, context-sensitive analysis during the design process and prior to implementation. Finally, we discuss the inherently quantitative methods of analysis afforded by stochastic simulations as well as the potential for ethical discussions and how simulations with AI can improve traditional forms of thought experiments and future-oriented technology assessment.

翻译：当前围绕医疗保健中人工智能（AI）应用的伦理辩论，主要从三个维度将AI视为技术产物：其一，借助伦理清单评估现有AI产品的风险与潜在收益；其二，提出与辅助技术设计开发相关的预设伦理价值观清单；其三，推动将道德推理作为自动化过程组成部分的AI技术。据此，我们提出第四种方法，即将AI作为辅助伦理反思的方法论工具。我们提出一个由三个独立要素构成的AI模拟概念：1）基于行为数据的随机人类行为模型，用于模拟真实场景；2）关于内部政策价值陈述的定性经验数据；3）有助于理解这些变量变化影响的可视化组件。该方法有望为跨学科领域揭示具体情境中的预期伦理挑战或伦理权衡，进而引发对设计与实施方案的重新评估。这对于涉及极端复杂价值观与行为、或面临受影响人群（如痴呆症患者或认知障碍者护理对象）沟通资源受限的应用场景尤为有用。模拟虽不能替代伦理反思，但可在设计阶段及实施前实现细致的情境敏感分析。最后，我们探讨了随机模拟所固有的量化分析方法及其伦理讨论潜力，并阐明基于AI的模拟如何改进传统思想实验与前瞻性技术评估范式。