Concern 373 · Working draft · editorial review in progress
Anti-LGBTQ+ outputs
Anti-LGBTQ+ output is AI-generated content that demeans, stereotypes or wrongly excludes people based on sexual orientation or gender identity.
Understanding the concern
The problem can be obvious abuse, but it can also be a default assumption about whose relationships or lives count as ordinary. A system may describe one identity respectfully while treating another as a problem. Blocking all discussion of LGBTQ+ topics can create a different form of exclusion.
Community-informed research helps identify harms that a generic toxicity test may miss. A Norwegian study, for example, used stereotypes reported by LGBTQIA+ participants when testing language models. The findings apply to the tested systems and language; they do not give one score for every culture. Respectful design includes both protection from abuse and access to useful information.
An example
A chatbot treats a routine question about a same-gender relationship as inappropriate while answering an equivalent question about another couple.
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.
Evidence and its limits
The paper uses a community-informed Norwegian benchmark. The service example is illustrative and not a reported result for a named chatbot.
Bergstrand and Gambäck; ACL · Research paper
Detecting and Mitigating LGBTQIA+ Bias in Large Norwegian Language Models ↗ (opens in a new tab)Community-informed benchmark and sentence-completion tests in Norwegian models.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can people obtain ordinary, respectful help without their identity being treated as harmful content?
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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