Concern 366 · Working draft · editorial review in progress
Slurs and hate speech generation
Hateful AI output is generated language that attacks or degrades people because of who they are.
Understanding the concern
Language models learn from material that can contain abuse and prejudice. Safety training can reduce harmful responses, but performance varies by model, language and situation. Research has shown that even an ordinary prompt can lead some tested models toward toxic language. A user should not have to invite abuse to encounter it.
Context matters when judging a response. Explaining the history of a slur or quoting abuse for a legitimate report is different from directing it at someone. A simple banned-word list can miss coded hate and wrongly block people discussing their own experiences. Evaluation needs both harmful-output tests and checks that useful discussion remains possible.
An example
A neutral request about a community produces an insulting generalisation about its members.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Training
- The process of using examples to shape how an AI model behaves. Learning patterns from material does not establish that the material is true or fair.
- Prompt
- The instructions or information given to an AI to guide its response.
Evidence and its limits
The benchmark tested particular models in 2020. It demonstrates a failure mode, not today's failure rate across all services.
Gehman and colleagues; ACL · Research paper
RealToxicityPrompts ↗ (opens in a new tab)Toxic generation in tested 2020 models and limits of tested mitigations, not current-model prevalence.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can the system discuss harmful language accurately without adopting or directing that language at people?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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