This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

本文介绍了一个协作的、以人为中心的人工智能、算法和自动化伤害分类法。我们认为现有的分类法虽然有价值，但可能过于狭窄、不清晰，通常只为从业人员和政府服务，往往忽视了更广大公众的需求。通过借鉴现有分类法和大量有记录的事件，我们提出了一个可以广泛理解和适应各种受众的分类法，同时又具有灵活性、可扩展性和互操作性。通过与专家的反复完善和众包标注测试，我们提出的分类法可以成为民间组织、教育工作者、决策者、产品团队和一般公众的有力工具。通过增进对人工智能和相关技术实际伤害的理解，我们旨在提高认识，赋予非政府组织和个人识别和报告违规行为的能力，推动政策讨论，促进负责任的技术发展和应用。

合作的、以人为中心的人工智能、算法和自动化危害分类