Title: The Generative AI Ethics Playbook Show Chinese title

Title: 生成式人工智能伦理指南

Abstract: The Generative AI Ethics Playbook provides guidance for identifying and mitigating risks of machine learning systems across various domains, including natural language processing, computer vision, and generative AI. This playbook aims to assist practitioners in diagnosing potential harms that may arise during the design, development, and deployment of datasets and models. It offers concrete strategies and resources for mitigating these risks, to help minimize negative impacts on users and society. Drawing on current best practices in both research and ethical considerations, this playbook aims to serve as a comprehensive resource for AI/ML practitioners. The intended audience of this playbook includes machine learning researchers, engineers, and practitioners who are involved in the creation and implementation of generative and multimodal models (e.g., text-to-text, image-to-image, text-to-image, text-to-video). Specifically, we provide transparency/documentation checklists, topics of interest, common questions, examples of harms through case studies, and resources and strategies to mitigate harms throughout the Generative AI lifecycle. This playbook was made collaboratively over the course of 16 months through extensive literature review of over 100 resources and peer-reviewed articles, as well as through an initial group brainstorming session with 18 interdisciplinary AI ethics experts from industry and academia, and with additional feedback from 8 experts (5 of whom were in the initial brainstorming session). We note that while this playbook provides examples, discussion, and harm mitigation strategies, research in this area is ongoing. Our playbook aims to be a practically useful survey, taking a high-level view rather than aiming for covering the entire existing body of research. Show Chinese abstract

Abstract: 生成式人工智能伦理手册为识别和减轻机器学习系统在各个领域（包括自然语言处理、计算机视觉和生成式人工智能）中的风险提供了指导。 本手册旨在帮助从业者诊断在数据集和模型的设计、开发和部署过程中可能出现的潜在危害。 它提供了具体的策略和资源来减轻这些风险，以帮助减少对用户和社会的负面影响。 结合当前研究和伦理考量的最佳实践，本手册旨在成为人工智能/机器学习从业者的全面参考资料。 本手册的目标读者包括参与生成式和多模态模型（例如文本到文本、图像到图像、文本到图像、文本到视频）创建和实施的机器学习研究人员、工程师和从业者。 具体而言，我们提供了透明度/文档检查清单、关注主题、常见问题、通过案例研究展示的危害示例，以及在整个生成式人工智能生命周期中减轻危害的资源和策略。 本手册是在16个月的协作过程中完成的，期间对超过100份资源和同行评审文章进行了广泛的文献综述，并通过与来自产业和学术界的18位跨学科人工智能伦理专家进行初步头脑风暴会议，以及另外8位专家（其中5位参与了最初的头脑风暴会议）提供的反馈意见。 我们注意到，尽管本手册提供了示例、讨论和危害缓解策略，但该领域的研究仍在继续。 我们的手册旨在成为一项实际有用的概述，采取高层次的视角，而不是旨在涵盖现有的全部研究成果。