Critical infrastructure component failures when integrated with AI systems

Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems

Gipiškis et al. (2024)

Source DOI

Sub-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.

"When relying on GPAI in critical infrastructure, there may be common mode failures that begin with vulnerabilities or robustness issues in the underlying model architecture or training setup. These failures may happen accidentally (in edge-cases) or due to adversarial inputs to the AI systems [58]."(p. 46)

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

Supporting Evidence (1)

"For example, a failure in the scheduling software of a chemical plant caused by an adversarial keyword can cause damage to physical property through halting critical processes (e.g., cooling, mixing of reactants)."(p. 46)