BackChallenges in perceiving, measuring, and recognizing harm

Challenges in perceiving, measuring, and recognizing harm

A Taxonomy of Systemic Risks from General-Purpose AI

Uuk et al. (2025)

Sub-category

Risk Domain

Inadequate regulatory frameworks and oversight mechanisms that fail to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.

"Harm from AI often manifests subtly or over the long term, making it difficult to identify, measure, and address effectively."(p. 12)

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 →