The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios.

翻译：生成式人工智能的兴起及其在社会中的广泛采用，引发了关于其伦理影响及风险的激烈辩论。这些风险通常有别于传统判别式机器学习所涉及的风险。为综合近期相关学术讨论并厘清其规范性概念，我们针对生成式人工智能（尤其是大型语言模型和文本到图像模型）的伦理问题开展了一项范围综述。我们的分析构建了涵盖19个主题领域、包含378项规范性议题的分类体系，并根据其在文献中的出现频率进行排序。本研究为学者、从业者或政策制定者提供了综合性全景概览，浓缩了关于公平性、安全性、有害内容、幻觉、隐私、交互风险、安全对齐、社会影响等伦理辩论。我们讨论了研究结果，评估了文献中的关注失衡现象，并探索了缺乏实证支撑的风险场景。