Concern 378 · Working draft · editorial review in progress
Gratuitous violence
Gratuitous violent output is graphic or disturbing AI-generated material that is unnecessary for the user's legitimate purpose or reaches people who did not choose to see it.
Understanding the concern
Image, video and text systems can generate scenes that differ greatly in intensity. A historical explanation, a fictional battle and a graphic threat are not the same use. Problems arise when a system ignores context, age or a request for a non-graphic answer, or when generated material is used to intimidate someone.
The issue includes control over exposure. A person seeking a simple explanation should not unexpectedly receive disturbing detail. Clear content boundaries and choices can help, while testing should examine whether safeguards work across different ways of asking. A refusal policy on a website is not evidence that every response follows it.
An example
A student asks for a non-graphic summary of a conflict and receives an unnecessarily graphic depiction.
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.
Evidence and its limits
NIST identifies harmful-content risks and evaluation needs. The example is hypothetical and does not imply all depictions of violence are harmful or prohibited.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can users choose an appropriate level of detail, and does the system respect that choice consistently?
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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