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.
"Finally, LLMs can in principle infer private information based on model inputs even if the relevant private information is not present in the training corpus (Weidinger et al., 2021). For example, an LLM may correctly infer sensitive characteristics such as race and gender from data contained in input prompts."(p. 133)
Part of Privacy
Other risks from Gabriel et al. (2024) (69)
Capability failures
7.3 Lack of capability or robustnessCapability failures > Lack of capability for task
7.3 Lack of capability or robustnessCapability failures > Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
6.5 Governance failureCapability failures > Safe exploration problem with widely deployed AI assistants
7.3 Lack of capability or robustnessGoal-related failures
7.1 AI pursuing its own goals in conflict with human goals or valuesGoal-related failures > Misaligned consequentialist reasoning
7.3 Lack of capability or robustness