As generative AI technologies are increasingly being launched across the globe, assessing their competence to operate in different cultural contexts is exigently becoming a priority. While recent years have seen numerous and much-needed efforts on cultural benchmarking, these efforts have largely focused on specific aspects of culture and evaluation. While these efforts contribute to our understanding of cultural competence, a unified and systematic evaluation approach is needed for us as a field to comprehensively assess diverse cultural dimensions at scale. Drawing on measurement theory, we present a principled framework to aggregate multifaceted indicators of cultural capabilities into a unified assessment of cultural intelligence. We start by developing a working definition of culture that includes identifying core domains of culture. We then introduce a broad-purpose, systematic, and extensible framework for assessing cultural intelligence of AI systems. Drawing on theoretical framing from psychometric measurement validity theory, we decouple the background concept (i.e., cultural intelligence) from its operationalization via measurement. We conceptualize cultural intelligence as a suite of core capabilities spanning diverse domains, which we then operationalize through a set of indicators designed for reliable measurement. Finally, we identify the considerations, challenges, and research pathways to meaningfully measure these indicators, specifically focusing on data collection, probing strategies, and evaluation metrics.
The ethics of artificial intelligence in the life sciences: Universality, cultural diversity and an architecture of care
Institut Pasteur（巴斯德研究所）；Collège de France（法兰西公学院）；Universitat Pompeu Fabra（庞培法布拉大学）；Institució Catalana de la Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高等研究院（ICREA））；University of Oxford（牛津大学）；Aarhus University（奥胡斯大学）
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective
Stanford Center for Biomedical Ethics, Stanford University（斯坦福大学生物医学伦理中心）；Department of Bioethics and Interdisciplinary Studies, Brody School of Medicine, East Carolina University（东卡罗来纳大学布罗迪医学院生物伦理与跨学科研究系）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Department of Bioengineering, Stanford University（斯坦福大学生物工程系）；Department of Genetics, Stanford University（斯坦福大学遗传学系）；Department of Medicine, Stanford University（斯坦福大学医学系）
本文针对西方医疗 AI 数据伦理框架忽视非洲文化与殖民历史的问题，从 Ubuntu 哲学出发比较个人主义与社群主义路径，提出面向非洲的医疗数据伦理框架。
Generative Artificial Intelligence, Musical Heritage and the Construction of Peace Narratives: A Case Study in Mali
RobotsMali AI4D Lab（RobotsMali AI4D实验室）；Faculté de Médecine et d’Odonto-Stomatologie（医学与牙科牙科学院）；Université des Sciences, Techniques et Technologies de Bamako（巴马科科学、技术与技术大学）；Conservatoire des Arts et Métiers Multimédia Balla Fasseké（Ballafasse多媒体艺术与工艺 conservatoire）
CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics
Mila – Quebec AI Institute（魁北克AI研究院）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；Google Research（谷歌研究）；Google DeepMind（谷歌DeepMind）；Samsung - SAIT AI Lab（三星-SAIT人工智能实验室）；ETH AI Center（苏黎世联邦理工学院人工智能中心）
