MIT AI Risk Repository · Risk Sub-Category · 11.02.02

Economic loss

Category: Allocative Harms

Open the MIT Risk Navigator All entries from Shelby2023

Description

Financial harms [52, 160] co-produced through algorithmic systems, especially as they relate to lived experiences of poverty and economic inequality... demonetization algorithms that parse content titles, metadata, and text, and it may penalize words with multiple meanings [51, 81], disproportionately impacting queer, trans, and creators of color [81]. Differential pricing algorithms, where people are systematically shown different prices for the same products, also leads to economic loss [55]. These algorithms may be especially sensitive to feedback loops from existing inequities related to e

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
Reifying essentialist categories Risk Sub-Category · 1.1
Allocative Harms Risk Category · 1.1
Opportunity 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