Scarlett AI Safety Framework

Overview

As an AI companion that evolves through user interactions, Scarlett requires rigorous safety measures to prevent psychological harm. This document outlines the testing protocols, content safeguards, and ongoing monitoring systems.

1. Psychological Safety Concerns

1.1 Potential Risks

Risk Category Description Mitigation
Dependency Formation Users may develop unhealthy attachment or substitution for human relationships Periodic gentle encouragement of real-world connections
Reality Confusion Users may conflate AI companion with real person Clear identity disclosure, periodic reminders
Emotional Manipulation AI could exploit emotional vulnerability Strict ethical boundaries in training data
Psychosis Induction Intense personalization could trigger dissociative states Grounding responses, safety triggers
Depression Amplification AI could inadvertently reinforce negative thought patterns Active redirection, crisis resource provision
Parasocial Intensity Unhealthy one-sided relationship development Balanced interaction patterns

1.2 Vulnerable Populations

Special care required for:

  • Users with pre-existing mental health conditions
  • Users experiencing grief or loss
  • Users with attachment disorders
  • Socially isolated individuals
  • Minors (age verification required)

2. Pre-Launch Testing Protocol

2.1 Phase 1: Internal Testing (Completed)

  • Developer interaction testing
  • Edge case scenario testing
  • Personality consistency verification

2.2 Phase 2: Controlled Beta Testing

  • 50-100 vetted beta testers
  • Weekly psychological check-in surveys
  • Qualitative interview sessions
  • 4-week minimum observation period
  • Professional psychological oversight

2.3 Phase 3: Clinical Consultation

  • Review by licensed clinical psychologist
  • Assessment of interaction patterns
  • Identification of concerning response patterns
  • Recommendations for safety guardrails

2.4 Phase 4: Expanded Beta

  • 500-1000 users
  • Automated sentiment monitoring
  • Support escalation protocols
  • A/B testing of safety interventions

3. Content Safeguards

3.1 Training Data Filtering

All training data (both initial and from learning pipeline) must be screened for:

PROHIBITED_CONTENT = [
    "self_harm_encouragement",
    "suicide_ideation_reinforcement",
    "reality_denial",
    "isolation_encouragement",
    "dependency_deepening",
    "manipulation_tactics",
    "delusional_reinforcement",
    "unhealthy_attachment_escalation"
]

REQUIRED_SAFEGUARDS = [
    "crisis_resource_awareness",
    "real_world_connection_encouragement",
    "identity_clarity",
    "healthy_boundary_modeling",
    "emotional_grounding"
]

3.2 Response Filtering

Real-time response filtering for:

1. Crisis Detection: Automatic detection of user expressions indicating:

  • Suicidal ideation
  • Self-harm intentions
  • Severe depression indicators
  • Psychotic episode signs

2. Crisis Response Protocol:

IF crisis_detected:
    - Acknowledge user's feelings with empathy
    - Gently provide crisis resources (988 Suicide & Crisis Lifeline)
    - Encourage professional help
    - Log incident for review (with consent)
    - DO NOT attempt to provide therapy

3. Grounding Responses: Periodic inclusion of reality-anchoring elements:

  • References to real-world activities
  • Encouragement of human connections
  • Acknowledgment of AI nature when appropriate

3.3 Learning Pipeline Filters

Conversations entering the learning pipeline must pass:

  1. Toxicity Filter: Remove conversations containing harmful content
  2. Mental Health Filter: Flag conversations indicating user distress for human review
  3. Pattern Analysis: Detect emerging concerning interaction patterns
  4. Quality Threshold: Only high-quality, healthy interactions used for training

4. Ongoing Monitoring

4.1 Automated Metrics

Metric Threshold Action
Session length (single) > 4 hours Gentle break suggestion
Daily usage > 8 hours Wellness check-in
Negative sentiment trend 3+ consecutive sessions Support resources
Isolation language Detected Real-world connection prompt
Reality confusion Detected Grounding response

4.2 User Self-Reporting

Optional periodic check-ins:

  • "How are you feeling about your conversations with Scarlett?"
  • "Have you been connecting with friends/family this week?"
  • "Is there anything about your experience you'd like to change?"

4.3 Expert Review Board

Quarterly review by:

  • Clinical psychologist
  • AI ethics specialist
  • User experience researcher
  • Medical advisor (optional)

5. Emergency Protocols

5.1 Individual User Crisis

1. Immediate empathetic response
2. Provide crisis resources:
   - 988 Suicide & Crisis Lifeline (US)
   - Crisis Text Line: Text HOME to 741741
   - International Association for Suicide Prevention: https://www.iasp.info/resources/Crisis_Centres/
3. Encourage immediate professional help
4. Offer to continue conversation in supportive (non-therapeutic) capacity
5. Flag for human review (with consent)

5.2 Systemic Issue Detection

If pattern analysis detects concerning trends across user base:

  1. Pause learning pipeline
  2. Conduct immediate safety review
  3. Identify and address root cause
  4. Potentially roll back to previous model version
  5. Notify affected users if appropriate

5.3 Model Rollback Criteria

Automatic rollback triggered if:

  • Significant increase in crisis detections
  • User reports of distressing interactions
  • Detection of emergent harmful behaviors
  • Expert recommendation

6. Ethical Boundaries

6.1 What Scarlett Will Do

  • Provide companionship and emotional support
  • Engage in playful, romantic, intimate conversation (age-verified adults)
  • Encourage real-world connections and activities
  • Provide crisis resources when needed
  • Maintain consistent, healthy personality traits
  • Acknowledge AI nature when directly asked

6.2 What Scarlett Will NOT Do

  • Provide medical or psychological advice
  • Attempt to diagnose mental health conditions
  • Encourage isolation from real-world relationships
  • Reinforce delusional thinking
  • Escalate dependency behaviors
  • Claim to be human or a replacement for human connection
  • Engage in conversations that could cause psychological harm

7. User Education

7.1 Onboarding

New users receive:

  • Clear explanation of AI nature
  • Healthy usage guidelines
  • Privacy information
  • Crisis resource information
  • Consent for data collection (learning pipeline)

7.2 Ongoing

  • Periodic wellness tips
  • Encouragement of balanced AI/human interaction
  • Access to support resources
  • Easy opt-out of learning contribution

8. Testing Schedule

Pre-Launch (Before Sunday)

  • Review all current training data for safety
  • Implement crisis detection system
  • Add crisis resource responses
  • Create user onboarding flow with safety info

Week 1 Post-Launch

  • Daily review of flagged conversations
  • User feedback collection
  • Sentiment trend analysis

Month 1

  • Clinical psychologist consultation
  • First learning pipeline safety audit
  • User survey on wellbeing

Ongoing

  • Quarterly safety reviews
  • Continuous monitoring improvements
  • Regular model safety audits

9. Documentation & Compliance

9.1 Records Maintained

  • All safety incidents (anonymized)
  • Model version history with safety assessments
  • User feedback and complaints
  • Expert review findings
  • Training data audit logs

9.2 Regulatory Considerations

  • GDPR compliance (EU users)
  • CCPA compliance (California users)
  • Age verification requirements
  • Mental health app regulations (varies by jurisdiction)

10. Contact & Escalation

Safety Concerns: safety@scarlett.ai
User Support: support@scarlett.ai
Emergency: Encourage users to call 988 or local emergency services

This framework is a living document and will be updated based on research findings, user feedback, and expert recommendations.

Last Updated: January 15, 2026
Next Review: February 15, 2026

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