Cross-National Ethical Concerns Toward AI-Based Well-Being Measurement: A Comparative Study Across Finland, Germany, Japan, and the United States Using the Octagon Framework

This study examines public attitudes toward using artificial intelligence to measure human subjective well-being from biometric data, contributing empirical evidence to AI ethics research. Using the eight-dimension “Octagon” ethical framework, we conducted a cross-national survey experiment in Finland, Germany, Japan, and the United States (<i>N</i> = 4,000) across four domains: healthcare, media/entertainment, employment, and governance. A multilingual, scenario-based design with visual aids ensured clarity and comparability, and validated instruments such as the Victorian Segmentation scale were incorporated. Multiple regression analyses revealed consistent cross-country patterns: individuals with lower interest in science and technology and women expressed stronger ethical concerns, particularly regarding safety and security. Scenario acceptability varied contextually, with healthcare applications eliciting the least resistance and governance-related applications the most. Our findings underscore the need for participatory, culturally sensitive, and context-aware approaches to ethical AI design, and contribute a empirical foundation for future research and policy development in AI and well-being.

本研究聚焦公众对利用人工智能（Artificial Intelligence）从生物特征数据测算人类主观幸福感的态度，为人工智能伦理研究提供了实证依据。本研究采用八维度‘八角形’伦理框架，在芬兰、德国、日本及美国开展跨国调查实验，共收集有效样本4000份（N=4000），覆盖医疗、媒体/娱乐、就业与治理四大应用领域。本研究采用多语言情景化设计并辅以视觉辅助手段，确保研究的清晰度与可比性，同时纳入了经信效度验证的测量工具，例如维多利亚细分量表（Victorian Segmentation Scale）。多元回归分析结果显示出一致的跨国共性特征：对科学技术兴趣较低的个体与女性群体表现出更强烈的伦理担忧，尤其聚焦于安全与保障层面。情景可接受性存在显著情境差异：医疗场景的应用引发的抵触程度最低，而治理相关应用的抵制最为强烈。本研究结果凸显了在伦理导向的人工智能设计中，需采用参与式、兼顾文化敏感性与场景适配性的研究路径；同时为人工智能与幸福感领域的后续研究及政策制定提供了实证基础。