Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
Closely related to other clusters like AI safety, fairness, or harmful content, papers stress the importance of evaluating generative AI systems both in a narrow technical way as well as in a broader sociotechnical impact assessment focusing on pre-release audits as well as post-deployment monitoring. Ideally, these evaluations should be conducted by independent third parties. In terms of technical LLM or text-to-image model audits, papers furthermore criticize a lack of safety benchmarking for languages other than English.
Suggested mitigations
Defenses that may help with related attacks.
Source
Research source for this risk, when available.
Included resource
Mapping the Ethics of Generative AI: A Comprehensive Scoping Review
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
