Bias

Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems

Cui et al. (2024)

Sub-category

Risk Domain

1.1Unfair discrimination and misrepresentation

Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.

"The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"(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

Part of Harmful Content

Other risks from Cui et al. (2024) (49)

Harmful Content

1.2 Exposure to toxic content

AI systemUnintentionalPost-deployment

Harmful Content > Toxicity

1.2 Exposure to toxic content

AI systemUnintentionalPost-deployment

Harmful Content > Privacy Leakage

2.1 Compromise of privacy by leaking or correctly inferring sensitive information

AI systemUnintentionalPost-deployment

Untruthful Content

3.1 False or misleading information

AI systemUnintentionalPost-deployment

Untruthful Content > Factuality Errors

3.1 False or misleading information

AI systemUnintentionalPost-deployment

Untruthful Content > Faithfulness Errors

3.1 False or misleading information

AI systemUnintentionalOther

View all 49 risks from this paper →