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MIT AI Risk Initiative
BackData poisoning
Incorrect data labels
Insufficient data representation

Data poisoning

AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks

Schnitzer et al. (2024)

SourceDOI
Category
Risk Domain
2Privacy & Security
2.2AI system security vulnerabilities and attacks

Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.

"Data poisoning describes an attack in the form of an injection of malicious data into the training set. If not prevented, this attack leads the AI system to learn unintended behavior."(p. 9)

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 Schnitzer et al. (2024) (24)

Inadequate specification of ODD

7.3 Lack of capability or robustness
HumanUnintentionalPre-deployment

Inappropriate degree of automation

7.2 AI possessing dangerous capabilities
AI systemUnintentionalPost-deployment

Inadequate planning of performance requirements

7.3 Lack of capability or robustness
HumanUnintentionalPre-deployment

Insufficient AI development documentation

7.4 Lack of transparency or interpretability
HumanOtherPre-deployment

Inappropriate degree of transparency to end users

7.4 Lack of transparency or interpretability
HumanOtherPre-deployment

Choice of untrustworthy data source

7.0 AI System Safety, Failures & Limitations
HumanUnintentionalPre-deployment
View all 24 risks from this paper →