# Why AI Harms Can't Be Fixed One Identity at a Time: What 5300 Incident Reports Reveal About Intersectionality

arXiv: https://arxiv.org/abs/2604.24519 · HTML: https://mlsanity.com/paper/2604.24519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 91 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval](https://mlsanity.com/paper/2407.20371/) (2024)
- [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022)
- [Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals](https://mlsanity.com/paper/2202.10848/) (2022)
- [Lazy Data Practices Harm Fairness Research](https://mlsanity.com/paper/2404.17293/) (2024)
- [Atlas of AI Risks: Enhancing Public Understanding of AI Risks](https://mlsanity.com/paper/2502.05324/) (2025)
- [Standardised schema and taxonomy for AI incident databases in critical digital infrastructure](https://mlsanity.com/paper/2501.17037/) (2025)
- [From Incidents to Insights: Patterns of Responsibility following AI Harms](https://mlsanity.com/paper/2505.04291/) (2025)
- [ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios](https://mlsanity.com/paper/2510.10998/) (2025)
- [Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database](https://mlsanity.com/paper/2409.15319/) (2024)
- [Should AI Mimic People? Understanding AI-Supported Writing Technology Among Black Users](https://mlsanity.com/paper/2505.00821/) (2025)
- [AI for All: Identifying AI incidents Related to Diversity and Inclusion](https://mlsanity.com/paper/2408.01438/) (2024)
- [The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform](https://mlsanity.com/paper/2504.06016/) (2025)
- [Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment](https://mlsanity.com/paper/2602.08565/) (2026)

## Abstract (arXiv)

AI risk assessment is the primary tool for identifying harms caused by AI systems. These include intersectional harms, which arise from the interaction between identity categories (e.g., class and skin tone) and which do not occur, or occur differently, when those categories are considered separately. Yet existing AI risk assessments are still built around isolated identity categories, and when intersections are considered, they focus almost exclusively on race and gender. Drawing on a large-scale analysis of documented AI incidents, we show that AI harms do not occur one identity category at a time. Using a structured rubric applied with a Large Language Model (LLM), we analyze 5,300 reports from 1,200 documented incidents in the AI Incident Database, the most curated source of incident data. From these reports, we identify 1,513 harmed subjects and their associated identity categories, achieving 98% accuracy. At the level of individual categories, we find that age and political identity appear in documented AI harms at rates comparable to race and gender. At the level of intersecting categories, harm is amplified up to three times at specific intersections: adolescent girls, lower-class people of color, and upper-class political elites. We argue that intersectionality should be a core component of AI risk assessment to more accurately capture how harms are produced and distributed across social groups.