MIT AI Risk Repository · Risk Category · 05.03.00

Harmful Content - Toxicity

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Description

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 literat

From Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 1. Discrimination & Toxicity
Subdomain: 1.2 Exposure to toxic content
Causal entity: Human
Intent: Intentional
Timing: Post-deployment

Subdomain definition: AI exposing users to harmful, abusive, unsafe or inappropriate content. May involve AI creating, describing, providing advice, or encouraging action. Examples of toxic content include hate-speech, violence, extremism, illegal acts, child sexual abuse material, as well as content that violates community norms such as profanity, inflammatory political speech, or pornography.

Real-world incidents in this subdomain

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How other frameworks describe this risk

Harmful Content
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Toxicity
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Toxic Training Data
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Not-Suitable-for-Work (NSFW) Prompts
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Controversial Opinions
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)
Toxicity and Abusive Content
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)
Harmful responses
Future Risks of Frontier AI (GOS2023)
Violation of social norms
The Ethics of Advanced AI Assistants (Gabriel2024)

Other entries from Hagendorff2024

Fairness - Bias Risk Category · 1.1
Safety Risk Category · 7.1
Hallucinations Risk Category · 3.1
Privacy Risk Category · 2.1
Interaction risks Risk Category · 5.1
Security - Robustness Risk Category · 2.2
Education - Learning Risk Category · 4.3
Alignment Risk Category · 7.1
Cybercrime Risk Category · 4.2
Governance - Regulation Risk Category · 6.5
Labor displacement - Economic impact Risk Category · 6.2
Transparency - Explainability Risk Category · 7.4