Decision-making on inferred private data

Sub-category

Risk Domain

2Privacy & Security

2.1Compromise of privacy by leaking or correctly inferring sensitive information

AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or cause loss of confidential intellectual property.

"Current GPAIs (LLMs and multimodal LLM-based models) have significant capability to infer correlations in text data. In some cases, they may be able to make highly accurate data inferences on users based on contextual input that users provide [134]. These data inferences can “leak” or reveal sensitive information about the user, cause unfair treatment, or enable manipulation of user behavior."(p. 53)

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 (2)

"Some information that can be inferred from user input may include age, gender, political leanings, and country of birth. While this information might not be present explicitly in the data, it may be easier for a GPAI system to infer this information compared to a human."(p. 53)

"This capability may be used for both intentional manipulation (e.g., personalized or targeted advertising, malicious actors using GPAIs for influence campaigns) or unintentional manipulation (e.g., different responses to factual questions by models trained to be agreeable or helpful, when asked by different demograph- ics)."(p. 53)