Concern 592 · Working draft · editorial review in progress
Age-verification failures
Age-assurance failures occur when a service's checks do not reliably keep age-inappropriate AI experiences away from children.
Understanding the concern
An age statement on a website is not the same as an effective safeguard. A service may rely on a simple declaration or fail to respond when a conversation reveals that a user is young. At the same time, intrusive checks can collect more personal data than necessary.
The challenge is to protect children while respecting privacy and access. eSafety's review of companion services examined reported safeguards and gaps. Age assurance needs to be paired with safer design throughout the product; it should not become an excuse to make everything behind a gate unsafe or to assume that every user was correctly classified.
An example
A service presents adult-oriented interactions after relying on a weak age check.
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
The review concerns four services and their reported measures. It does not establish the accuracy of every age-assurance method.
Australian eSafety Commissioner · March 2026 · Regulatory transparency report
eSafety report shows AI companions are putting children at risk ↗ (opens in a new tab)Summarizes four services' responses; a bounded review of reported safeguards, not every chatbot.
UNICEF Innocenti · December 2025 · Rights-based guidance
Guidance on AI and children, version 3.0 ↗ (opens in a new tab)Child-centered design recommendations; not a causal study of developmental harm from all AI use.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How can a service assess age proportionately while keeping the experience safer if the check is wrong?
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.
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?
Seeing potential
Seeing potential means recognizing people's capacity to learn, contribute, and grow. It resists reducing someone to a score or to the tasks a machine can perform faster.
Use tools to support people's development. Give learners time, feedback, and real opportunities to practice. A prediction about someone's performance should not close off their future.
Ask together: How could this help people develop abilities they value?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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