A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
<|reference_start|>A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms: 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.<|reference_end|>
@article{abercrombie2024a,
    title={A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and
  Automation Harms},
    author={Gavin Abercrombie, Djalel Benbouzid, Paolo Giudici, Delaram
  Golpayegani, Julio Hernandez, Pierre Noro, Harshvardhan Pandit, Eva
  Paraschou, Charlie Pownall, Jyoti Prajapati, Mark A. Sayre, Ushnish Sengupta,
  Arthit Suriyawongkul, Ruby Thelot, Sofia Vei, Laura Waltersdorfer},
    journal={arXiv preprint arXiv:2407.01294},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01294},
    primaryClass={cs.LG cs.AI cs.CY}
}