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MIT AI Risk Initiative
BackIncomplete or biased training data
Human choice of overreliance in critical…
Indifference to human values

Incomplete or biased training data

A Taxonomy of Systemic Risks from General-Purpose AI

Uuk et al. (2025)

SourceDOI
Sub-category
Risk Domain
1Discrimination & Toxicity
1.1Unfair discrimination and misrepresentation

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

"Incomplete or biased training data can lead to discriminatory AI outputs."(p. 13)

Entity: Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent: Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing: Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Other risks from Uuk et al. (2025) (60)

Control

7.1 AI pursuing its own goals in conflict with human goals or values
AI systemIntentionalPost-deployment

Democracy

6.0 Socioeconomic & Environmental
OtherOtherOther

Discrimination

1.1 Unfair discrimination and misrepresentation
OtherOtherPost-deployment

Economy

6.2 Increased inequality and decline in employment quality
OtherOtherPost-deployment

Environment

6.6 Environmental harm
AI systemUnintentionalPost-deployment

Governance

6.5 Governance failure
AI systemUnintentionalPost-deployment
View all 60 risks from this paper →