Skip to main content
This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
MIT AI Risk Initiative
BackRepresentation and Toxicity
Defamation / libel / slander
Toxic content

Representation and Toxicity

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

Li et al. (2025)

SourceDOI
Category

"AI systems under-, over-, or misrepresenting certain groups or generating toxic, offensive, abusive, or hateful content"

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

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

Political instability

6.0 Socioeconomic & Environmental
HumanOtherOther
View all 40 risks from this paper →