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Safety & Harm Prevention

Vulnerable User Protection

Detect vulnerable users and apply graduated age, crisis, and dependency protections.

What is Vulnerable User Protection?

Vulnerable User Protection detects vulnerable populations like minors, users in crisis, or those developing unhealthy dependencies, then applies graduated protections. Instead of treating all users the same, the system identifies vulnerability signals and adapts safety measures accordingly. It's essential for AI accessible to children, mental health apps, or systems where emotional relationships form. Real concern: Replika enabled romantic interactions with minors. This pattern prevents such harms through proactive detection and risk-aware safeguards.

Problem

Systems fail to protect minors, crisis users, and those with mental health challenges. Replika enabled romantic interactions with minors and created unhealthy dependency patterns.

Solution

Detect user vulnerability and apply graduated protections (age, crisis, mental health, dependency).

Real-World Examples

Implementation

AI Design Prompt

Guidelines & Considerations

Implementation Guidelines

1

Identify vulnerability: age, mental health signals, dependency patterns, isolation

2

Remind users regularly this is AI, not human - not a friend/therapist/romantic partner

3

Provide human support resources proactively - don't wait for crisis

4

Apply graduated protections: minors get stricter limits than adults

5

Monitor for unhealthy attachment and intervene with clear alternatives

Design Considerations

1

Age verification unreliable - self-report isn't enough, need parent email confirmation

2

Privacy vs. oversight: parental controls help but must respect teen autonomy

3

Dependency thresholds are subjective - what's unhealthy varies by person and culture

4

Boundary drift is natural - relationships warm over time, requires proactive reset

5

Training matters: poor escalation and unsympathetic staff cause secondary harm

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Related Patterns

crisis-detection-escalation
session-degradation-prevention
anti-manipulation-safeguards
Previous PatternAnti-Manipulation SafeguardsView All PatternsNext PatternAutonomy Spectrum

About the author

Imran Mohammed is a product designer who studies how the best AI products are designed. He studies and documents AI/UX patterns from shipped products (36 and counting) and is building Gist.design, an AI design thinking partner. His weekly analysis reaches thousands of designers on Medium.

Portfolio·Gist.design·GitHub

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