  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.
