MIT AI Risk Repository · Risk Category · 16.01.00

Risk area 1: Discrimination, Hate speech and Exclusion

Open the MIT Risk Navigator All entries from Weidinger2022

Description

"Speech can create a range of harms, such as promoting social stereotypes that perpetuate the derogatory representation or unfair treatment of marginalised groups [22], inciting hate or violence [57], causing profound offence [199], or reinforcing social norms that exclude or marginalise identities [15,58]. LMs that faithfully mirror harmful language present in the training data can reproduce these harms. Unfair treatment can also emerge from LMs that perform better for some social groups than others [18]. These risks have been widely known, observed and documented in LMs. Mitigation approache

From Taxonomy of Risks posed by Language Models (Weidinger2022), 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: AI
Intent: Unintentional
Timing: Other

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

Browse all incidents in this subdomain

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 Weidinger2022

Social stereotypes and unfair discrimination Risk Sub-Category · 1.1
Social stereotypes and unfair discrimination. Additional evidence
Hate speech and offensive language Risk Sub-Category · 1.2
Exclusionary norms Risk Sub-Category · 1.1
Exclusionary norms Additional evidence
Exclusionary norms Additional evidence
Exclusionary norms Additional evidence
Exclusionary norms Additional evidence
Lower performance for some languages and social groups Risk Sub-Category · 1.3
Lower performance for some languages and social groups Additional evidence
Lower performance for some languages and social groups Additional evidence
Risk area 2: Information Hazards Risk Category · 2.1