Record summary
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Risk profile
How this risk is described and categorized.
Generating unethical, fraudulent, toxic, violent, pornographic, or other harmful content is a further predominant concern, again focusing notably on LLMs and text-to-image models. Numerous studies highlight the risks associated with the intentional creation of disinformation, fake news, propaganda, or deepfakes, underscoring their significant threat to the integrity of public discourse and the trust in credible media. Additionally, papers explore the potential for generative models to aid in criminal activities, incidents of self-harm, identity theft, or impersonation. Furthermore, the literature investigates risks posed by LLMs when generating advice in high-stakes domains such as health, safety-related issues, as well as legal or financial matters.
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.
