hip-0200

HIP-200: Responsible AI Principles and Commitments. Status Draft. Hanzo's own standard — read this before implementing against it.

HIP-0200: Responsible AI Principles and Commitments

Abstract

This HIP establishes the foundational Responsible AI framework for Hanzo AI. It defines our commitment to building AI systems that are safe, fair, transparent, and beneficial to humanity. All other AI ethics HIPs (HIP-201 through HIP-249) and sustainability HIPs (HIP-250 through HIP-299) reference this document as the canonical source for Hanzo's responsible AI commitments.

Mission and AI Ethics Thesis

Hanzo AI is committed to democratizing access to AI while ensuring these powerful systems remain under meaningful human control. Our thesis: responsible AI is not a constraint on innovation—it is a competitive advantage that builds trust with users, partners, and regulators. We believe AI should augment human capability, not replace human agency.

Core Principles

1. Safety First

AI systems must be demonstrably safe before deployment. We prioritize:

Harm prevention: Systems must not facilitate illegal activity, violence, or self-harm
Robustness: Systems must degrade gracefully under adversarial conditions
Controllability: Humans must be able to intervene, correct, or shut down AI systems
Testing rigor: Safety evaluations must precede capability expansions

2. Fairness and Non-Discrimination

AI systems must treat all users equitably:

Bias detection: Systematic testing across demographic groups
Outcome monitoring: Tracking disparate impact in production
Remediation: Clear process for addressing discovered biases
Inclusive design: Accessibility and multilingual support

3. Transparency and Explainability

Users deserve to understand AI behavior:

Model cards: Public documentation of capabilities and limitations
Decision explanations: Interpretable outputs where feasible
Training disclosure: Clear information about data sources and methods
Uncertainty communication: Systems must express confidence levels

4. Privacy and Data Protection

User data must be protected:

Data minimization: Collect only what is necessary
Consent: Clear opt-in for data use in training
Retention limits: Delete data when no longer needed
Security: State-of-the-art protection for stored data

5. Human Oversight

AI systems must support human control:

Human-in-the-loop: Critical decisions require human approval
Escalation paths: Clear routes from AI to human support
Override capability: Users can always request human assistance
Audit trails: Complete logs of AI decisions

6. Accountability

We take responsibility for our systems:

Clear ownership: Named individuals accountable for each system
Incident response: 24/7 capability to address safety issues
External review: Regular third-party audits
Public reporting: Transparent disclosure of incidents and mitigations

Material Topics

Safety & Security

| Topic | Materiality | Metrics | |-------|-------------|---------| | Jailbreak resistance | Critical | Attack success rate | | Hallucination rate | High | Factual accuracy % | | Harmful output | Critical | Safety filter effectiveness | | System availability | High | Uptime %, MTTR |

Fairness & Bias

| Topic | Materiality | Metrics | |-------|-------------|---------| | Demographic bias | High | Performance parity across groups | | Language bias | Medium | Quality consistency across languages | | Socioeconomic bias | High | Accessibility metrics |

Privacy & Data

| Topic | Materiality | Metrics | |-------|-------------|---------| | Training data consent | Critical | % data with clear consent | | PII handling | Critical | Incidents, exposure events | | Data retention | High | Compliance rate |

Environmental Impact

| Topic | Materiality | Metrics | |-------|-------------|---------| | Training emissions | High | tCO2e per model | | Inference efficiency | High | Tokens/kWh | | Hardware lifecycle | Medium | E-waste metrics |

Governance Structure

AI Ethics Board

| Role | Responsibility | |------|----------------| | Chief AI Ethics Officer | Strategic direction, external representation | | Model Risk Committee | Approval of high-risk deployments | | Safety Team | Red-teaming, incident response | | External Advisors | Independent review, academic perspective |

Decision Framework

| Risk Level | Examples | Approval Required | |------------|----------|-------------------| | Low | Minor model updates, bug fixes | Engineering lead | | Medium | New capabilities, expanded access | Model Risk Committee | | High | New model families, API changes | AI Ethics Board | | Critical | Safety-related changes | Board + external review |

Metrics and Targets

Safety Metrics

| Metric | Current | 2025 Target | Measurement | |--------|---------|-------------|-------------| | Jailbreak success rate | TBD | <1% | Red team testing | | Harmful output rate | TBD | <0.01% | Production monitoring | | Safety incident response time | TBD | <4 hours | Incident logs |

Fairness Metrics

| Metric | Current | 2025 Target | Measurement | |--------|---------|-------------|-------------| | Performance parity (demographic) | TBD | <5% gap | Benchmark testing | | Language quality parity | TBD | <10% gap | Human evaluation |

Environmental Metrics

| Metric | Current | 2025 Target | Measurement | |--------|---------|-------------|-------------| | Training emissions (tCO2e/model) | TBD | -30% | Carbon accounting | | Inference efficiency | TBD | +50% | Performance monitoring |

Verification and Assurance

Internal Testing

Pre-deployment: Full safety evaluation suite
Continuous: Production monitoring and alerting
Periodic: Quarterly red-team exercises

External Verification

| Type | Frequency | Provider | |------|-----------|----------| | Safety audit | Annual | Third-party AI safety firm | | Bias audit | Annual | Academic partner | | Security audit | Continuous | Bug bounty + penetration testing |

Standards Alignment

NIST AI RMF: Risk management framework
ISO/IEC 42001: AI management system (target certification)
OECD AI Principles: Policy alignment
EU AI Act: Compliance preparation

Known Tradeoffs

Capability vs. Safety

Tradeoff: More capable models may be harder to control
Mitigation: Safety investment scales with capability
Disclosure: We will document capability-safety frontier

Transparency vs. Security

Tradeoff: Full transparency may enable adversarial attacks
Mitigation: Responsible disclosure, staged transparency
Disclosure: We will explain what we withhold and why

Speed vs. Thoroughness

Tradeoff: Rapid iteration vs. comprehensive safety testing
Mitigation: Graduated rollouts, monitoring-based release
Disclosure: We will flag when releases have limited safety testing

Personalization vs. Privacy

Tradeoff: Better personalization requires more user data
Mitigation: On-device processing, differential privacy
Disclosure: We will explain data use clearly

Related HIPs

Responsible AI (HIP-200 to HIP-249)

HIP-201: Model Risk Management (MRM)
HIP-205: Data Governance & Consent
HIP-210: Safety Evaluation Suite
HIP-220: Bias & Fairness Testing
HIP-230: Human Oversight & Escalation
HIP-240: Transparency Reports

Sustainability (HIP-250 to HIP-299)

HIP-251: Green AI Compute Practices
HIP-260: Carbon-Aware Training & Inference
HIP-270: Privacy-Preserving ML
HIP-280: Post-Quantum Security Roadmap

Changelog

| Version | Date | Changes | |---------|------|---------| | 1.0 | 2025-12-16 | Initial draft |