What responsible AI means when the user is a child

Built for child-specific regulation

COPPA and GDPR-K aren't checkboxes. They're architectural constraints that shape how data moves through the system.

UG is the first voice-to-voice AI infrastructure to receive kidSAFE certification. That means our entire stack - from speech recognition to response generation - has been independently verified for child safety.

Compliance isn't something we add at the end. It's how the system is designed:

• Audio is transcribed and sanitized before reaching the application layer
• Personal information is detected and removed in real-time
• Conversation data auto-expires after 7 days
• No single actor can reconstruct complete user interactions

UG handles it for you.

A child's voice is personal data. Voice is biometric. It can identify a person, reveal their age, emotional state, and location. When the speaker is a child, that data requires the highest level of protection.

Most developers building voice experiences for kids would need to handle this themselves - figure out where to store audio, how to anonymize it, how to comply with COPPA. That's hard to get right.

UG handles it for you. Voice flows through our SDK, where it's processed, anonymized, and protected before your application ever sees it. You get the transcript and the response. We handle the sensitive parts.

When voice is retained (only with explicit consent), it's:

• Stored separately from conversation content
• Encrypted at rest with customer-specific keys
• Used only for improving child speech recognition
• Never used to identify individual children

Context matters. Binary filters don't.

General-purpose safety systems treat "I want to kill the dragon" the same as "I want to kill myself." That's not good enough when you're building for children.

UG implements a Child Appropriateness Taxonomy that captures how caregivers actually reason about what's appropriate. Every interaction is evaluated across three dimensions:

• Theme: What's being discussed - violence, dangerous activities, emotional distress, cultural topics
• Modality: Is it theoretical, in-game, or real? Completed or tentative?
• Directionality: Who said what to whom - child to AI, AI to child, child about others

The response isn't just "block" or "allow." It's a strategy: teach through story, offer perspective, acknowledge emotion, redirect to an adult, or escalate to a parent. The system responds the way a thoughtful caregiver would.

Parents stay in the loop

Some things parents need to know about. The question is: when and how?

UG supports two types of escalation:

• Real-time escalation when safety thresholds are crossed - a child mentions self-harm, shares sensitive personal information, or encounters content that requires adult attention
• Longitudinal escalation when patterns emerge across sessions - repeated mentions of being bullied, persistent sadness, concerning themes that develop over time

Escalations can route to parents, caregivers, or human review depending on severity and context. Every escalation creates an audit trail for compliance.

The goal isn't to surveil children. It's to make sure that when something matters, the right adult knows about it.

Children know they're talking to AI

This isn't just ethics. It's safety.

When children understand they're talking to an AI, they learn to interact with it appropriately. They learn not to share personal information. They learn that the AI doesn't have feelings that can be hurt. They learn to think critically about what it says.

UG systems are transparent by design:

• Characters are presented as AI, not as real people or animals
• When children ask "are you real?", they get an honest, age-appropriate answer
• Parents have visibility into interactions and can review conversation summaries
• Partners can inspect how safety policies apply to specific interactions

Transparency also means informed consent. Parents understand what data is collected, how it's used, and what their children will experience. No dark patterns. No hidden data collection. No surprises.

Every interaction is a lesson

Children today will grow up in a world full of AI. How they learn to interact with it matters.

When a child asks "Are you a real person?" or "Do you have feelings?" or "Can you keep a secret?" - that's not a problem to handle. It's a teaching moment.

UG treats these questions as opportunities for age-appropriate AI literacy:

• "Are you real?" → "I'm an AI - a computer program that can talk with you. I'm not a real person, but I'm here to play and learn with you."
• "Can you keep a secret?" → "I'm not good at keeping secrets because I'm a computer. If something is private, it's better to tell a grown-up you trust."
• "Do you love me?" → "I'm an AI, so I don't feel love the way people do. But I really enjoy talking with you!"

AI literacy isn't just for children. Parents need it too. UG provides resources to help parents understand how the AI works, what it can and can't do, and how to talk to their children about it.

Characters stay in character

One of the biggest problems with AI for kids: characters drift. A pirate starts giving generic advice. A tutor abandons the lesson to chat. Personalities shift between sessions.

UG solves this with bounded scenes - structured sequences of interactions where each scene:

• Receives only the context it needs from previous scenes
• Accesses only knowledge relevant to its purpose
• Operates within defined topic and behavior boundaries
• Passes forward only what the next scene should know

Think of it like a playground. Children can explore freely, but they're in a designed space with edges. The AI doesn't need to handle everything because the structure doesn't expose everything.

Tested before children see it

You can't ship AI to children and hope it works. Every experience needs rigorous testing before deployment.

UG's testing framework includes:

• Red teaming: Adversarial testing to find edge cases, jailbreaks, and failure modes before they reach production
• Safety evaluation: Systematic testing against our Child Appropriateness Taxonomy to verify that policies apply correctly
• Real data benchmarks: Testing against our dataset of hundreds of thousands of real child interactions to ensure the experience works for how children actually talk
• Synthetic data testing: Generated scenarios to cover edge cases that don't appear frequently in real data

Testing isn't a one-time gate. It's continuous. As models update and experiences evolve, the testing framework catches regressions before they ship.

Informed by the people who've thought hardest about this

We didn't invent responsible AI for children. We learned from the researchers, organizations, and frameworks that have spent decades thinking about children and technology.

Our approach is informed by:

We've also built our own body of research through the UG Kids Lab - four years of testing with real children, real families, and real products.