设计辅助技术的人工智能模拟中的伦理考虑因素

Evin Miser, Orcun Sarioguz
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摘要

目前关于在医疗保健领域使用人工智能(AI)的伦理辩论主要从三个方面探讨人工智能技术。首先,他们使用伦理清单评估当前人工智能产品的风险和潜在益处。其次,他们事先提出了与辅助技术的设计和开发相关的伦理价值清单。第三,他们主张将道德推理纳入人工智能的自动化流程。正如简要的文献综述所示,这三种观点在讨论中占主导地位。我们提出了第四种方法:将人工智能视为辅助道德反思的方法论工具。这涉及一个人工智能模拟概念,其中包含三个要素:1) 基于行为数据的随机人类行为模型,用于模拟现实场景;2) 有关内部政策价值声明的定性经验数据;3) 可视化组件,用于说明变量变化的影响。这种方法旨在为跨学科领域提供有关特定环境中预期的伦理挑战或权衡的信息,从而促使对设计和实施计划进行重新评估。这对于涉及复杂价值观和行为或沟通资源有限的应用尤其有用,例如老年痴呆症护理或认知障碍患者护理。最后,我们讨论了随机模拟所带来的定量分析方法,以及这些模拟在加强传统思想实验和面向未来的技术评估方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ethical Considerations in AI Simulations for Designing Assistive Technologies
Current ethical debates on the use of artificial intelligence (AI) in healthcare approach AI technology in three primary ways. First, they assess the risks and potential benefits of current AI-enabled products using ethical checklists. Second, they propose ex ante lists of ethical values relevant to the design and development of assistive technologies. Third, they advocate for incorporating moral reasoning into AI's automation processes. These three perspectives dominate the discourse, as evidenced by a brief literature summary. We propose a fourth approach: viewing AI as a methodological tool to aid ethical reflection. This involves an AI simulation concept informed by three elements: 1) stochastic human behavior models based on behavioral data for simulating realistic scenarios, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components to illustrate the impact of variable changes. This approach aims to inform an interdisciplinary field about anticipated ethical challenges or trade-offs in specific settings, prompting a re-evaluation of design and implementation plans. This is particularly useful for applications involving complex values and behaviors or limited communication resources, such as dementia care or care for individuals with cognitive impairments. While simulation does not replace ethical reflection, it allows for detailed, context-sensitive analysis during the design process and before implementation.Finally, we discuss the quantitative analysis methods enabled by stochastic simulations and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments.
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