Summary of A Collaborative, Human-centred Taxonomy Of Ai, Algorithmic, and Automation Harms, by Gavin Abercrombie et al.
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
by 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
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new taxonomy for AI-related harms that is human-centered, collaborative, and understandable to a broad audience. The authors argue that existing taxonomies are often narrow, unclear, and cater mainly to practitioners and government, overlooking the needs of the wider public. To address this gap, they draw on existing taxonomies and a large repository of documented incidents to develop a taxonomy that is clear, flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, the authors aim to create a tool that can empower civil society organizations, educators, policymakers, product teams, and the general public to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand the bad things that happen when AI is used. Right now, there are some taxonomies (like categories) for these harms, but they’re not very good because they’re only clear to people who already know what’s going on. The authors want to change this by making a taxonomy that’s easy to understand and use for anyone. They did this by looking at existing categories and adding examples of real-life incidents where AI was used to cause harm. The goal is to help organizations, teachers, government, product teams, and the general public understand what’s going on so they can report violations, inform policy decisions, and make sure technology is developed and used responsibly. |
