Unfair capability distribution

A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents

Li et al. (2025)

Source DOI

Sub-category

Risk Domain

1Discrimination & Toxicity

1.3Unequal performance across groups

Accuracy and effectiveness of AI decisions and actions are dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.

"Performing worse for some groups than others in a way that harms the worse-off group"

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 Li et al. (2025) (40)

Autonomy

5.2 Loss of human agency and autonomy

OtherOtherOther

Autonomy > Impersonation / identity theft

4.3 Fraud, scams, and targeted manipulation

HumanIntentionalPost-deployment

Misinformation Harms

3.1 False or misleading information

AI systemOtherPost-deployment

Representation and Toxicity

1.0 Discrimination & Toxicity

AI systemUnintentionalPost-deployment

IP / copyright / personality / rights loss

4.3 Fraud, scams, and targeted manipulation

HumanIntentionalPost-deployment

Autonomy / agency loss

5.2 Loss of human agency and autonomy

OtherOtherOther

View all 40 risks from this paper →