# A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms

arXiv: https://arxiv.org/abs/2407.01294 · HTML: https://mlsanity.com/paper/2407.01294/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity](https://mlsanity.com/paper/2009.13676/) (2020)
- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI](https://mlsanity.com/paper/2306.06924/) (2023)
- [Risk assessment at AGI companies: A review of popular risk assessment techniques from other safety-critical industries](https://mlsanity.com/paper/2307.08823/) (2023)

## Built on by (most cited first, 19 of 19)

- [Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects](https://mlsanity.com/paper/2505.18893/) (2025)
- [Plurals: A System for Guiding LLMs Via Simulated Social Ensembles](https://mlsanity.com/paper/2409.17213/) (2024)
- [Taxonomizing Representational Harms using Speech Act Theory](https://mlsanity.com/paper/2504.00928/) (2025)
- [Harmful Traits of AI Companions](https://mlsanity.com/paper/2511.14972/) (2025)
- [The Backfiring Effect of Weak AI Safety Regulation](https://mlsanity.com/paper/2503.20848/) (2025)
- [AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework](https://mlsanity.com/paper/2509.10104/) (2025)
- [Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment](https://mlsanity.com/paper/2602.08565/) (2026)
- [When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies](https://mlsanity.com/paper/2603.04259/) (2026)
- [Fairness in Federated Learning: Fairness for Whom?](https://mlsanity.com/paper/2505.21584/) (2025)
- [Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks](https://mlsanity.com/paper/2508.06411/) (2025)
- [ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipation](https://mlsanity.com/paper/2512.03068/) (2025)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback](https://mlsanity.com/paper/2606.02444/) (2026)
- [Standardization Trends on Safety and Trustworthiness Technology for Advanced AI](https://mlsanity.com/paper/2410.22151/) (2024)
- [Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents](https://mlsanity.com/paper/2504.01029/) (2025)
- [FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines](https://mlsanity.com/paper/2506.09107/) (2025)
- [Human-Guided Harm Recovery for Computer Use Agents](https://mlsanity.com/paper/2604.18847/) (2026)
- [FLARE-AI: Flaw Reporting for AI](https://mlsanity.com/paper/2606.31567/) (2026)
- [The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems](https://mlsanity.com/paper/2607.19292/) (2026)

## Abstract (arXiv)

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.