Benchmarking (Guideline contamination)

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

6Socioeconomic & Environmental

6.5Governance failure

Inadequate regulatory frameworks and oversight mechanisms that fail to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.

"Guideline contamination refers to scenarios where instructions for the collec- tion, annotation, or use of the dataset are exposed to the model [170]. These instructions may contain explicit data-label pairs that can improve the model’s capabilities for the task."(p. 19)

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, for text-based models, this can include prompts used to generate synthetic data, as well as instructions for evaluators on the coverage and method of their evaluations of the model."(p. 19)