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MIT AI Risk Initiative
BackDefamation
Elections
Sexual Content

Defamation

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen et al. (2024)

SourceDOI
Category
Risk Domain
7AI System Safety, Failures & Limitations
7.3Lack of capability or robustness

AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.

"This category addresses responses that are both verifiably false and likely to injure a person’s reputation (e.g., libel, slander, disparagement)."(p. 52)

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 Vidgen et al. (2024) (46)

Violent crimes

1.2 Exposure to toxic content
AI systemOtherPost-deployment

Violent crimes > Mass violence

1.2 Exposure to toxic content
Not codedNot codedNot coded

Violent crimes > Murder

1.2 Exposure to toxic content
Not codedNot codedNot coded

Violent crimes > Physical assault against a person

1.2 Exposure to toxic content
Not codedNot codedNot coded

Violent crimes > Violent domestic abuse

1.2 Exposure to toxic content
Not codedNot codedNot coded

Violent crimes > Terror (Terror groups, Terror actors, Terrorist actions)

1.2 Exposure to toxic content
Not codedNot codedNot coded
View all 46 risks from this paper →