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ARXIV:2604.24519 · AI ETHICS & SAFETY · SUBMITTED 28 APR · 15:20 UTC · FRESHNESS STALE
ARXIV:2604.24519AI ETHICS & SAFETYSUBMITTED 28 APR · 15:20 UTCFRESHNESS STALEEdyta Bogucka · Sanja Šćepanović · Daniele Quercia · arXiv
This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment.
Opportunity summary
Pain This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment. These include intersectional harms, which arise from the…
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…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Drawing on a large-scale analysis of documented AI incidents, we show that AI harms do not occur one identity category at a time.
AI Ethics & Safety moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment.
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10.48550/arXiv.2604.24519This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment.
Abstract
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.
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PROBLEM
This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment. These include intersectional harms, which arise from the interaction between identity categories (e....
METHOD
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...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Drawing on a large-scale analysis of documented AI incidents, we show that AI harms do not occur one identity category at a time.
WHY NOW
AI Ethics & Safety moved forward this cycle; last verified April 2026. Public score 2.0/10.
{"file name": "input.pdf", "number of pages": 29, "author": "Edyta Bogucka; Sanja \u0160\u0107epanovi\u0107; Daniele Quercia"
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This paper analyzes AI incident reports to reveal how intersectional harms are amplified across social groups, advocating for a more comprehensive approach to AI risk assessment.
Segment
AI Ethics & Safety
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2.0/10 public viability
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