MIT AI Risk Repository · Risk Sub-Category · 11.01.06

Reifying essentialist categories

Category: Representational Harms

Open the MIT Risk Navigator All entries from Shelby2023

Description

algorithmic systems that reify essentialist social categories can be understood as when systems that classify a person’s membership in a social group based on narrow, socially constructed criteria that reinforce perceptions of human difference as inherent, static and seemingly natural... especially likely when ML models or human raters classify a person’s attributes – for instance, their gender, race, or sexual orientation – by making assumptions based on their physical appearance

From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 1. Discrimination & Toxicity
Subdomain: 1.1 Unfair discrimination and misrepresentation
Causal entity: AI
Intent: Unintentional
Timing: Post-deployment

Subdomain definition: Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and representation of those groups.

Real-world incidents in this subdomain

Browse all incidents in this subdomain

How other frameworks describe this risk

Discrimination
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Harms of Representation and Other Biases
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Risks from bias and underrepresentation
International Scientific Report on the Safety of Advanced AI (Bengio2024)
Bias
International AI Safety Report 2025 (Bengio2025)
Bias
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Toxicity and Bias Tendencies
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Biased Training Data
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Broken systems
Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

Other entries from Shelby2023

Representational Harms Risk Category · 1.1
Stereotyping social groups Risk Sub-Category · 1.1
Demeaning social groups Risk Sub-Category · 1.1
Erasing social groups Risk Sub-Category · 1.3
Alienating social groups Risk Sub-Category · 1.1
Denying people the opportunity to self-identify Risk Sub-Category · 1.1
Allocative Harms Risk Category · 1.1
Opportunity loss Risk Sub-Category · 1.1
Economic loss Risk Sub-Category · 1.1
Quality-of-Service Harms Risk Category · 1.3
Alienation Risk Sub-Category · 1.3
Increased labor Risk Sub-Category · 1.3