A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
This provides a tool for civil society, educators, policymakers, and the public to better understand and address AI harms, though it is incremental as it builds on existing taxonomies.
The authors tackled the problem of existing taxonomies of AI harms being narrow and unclear by proposing a collaborative, human-centred taxonomy that is clear, flexible, and interoperable, developed through iterative refinement with experts and crowdsourced testing.
This paper introduces a collaborative, human-centred 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.