HIP-230: AI Transparency & Explainability. Status Draft. Hanzo's own standard — read this before implementing against it.
This HIP establishes the transparency and explainability framework for Hanzo AI systems. It defines requirements for communicating AI capabilities and limitations, providing explanations for AI outputs, and ensuring stakeholders can understand and audit AI behavior.
| Level | Audience | Depth | Examples | |-------|----------|-------|----------| | Public | General users | High-level | Product pages, blog posts | | User | Active users | Functional | In-product disclosures | | Developer | API users | Technical | API documentation | | Auditor | Reviewers | Detailed | Model cards, audit reports | | Regulator | Authorities | Comprehensive | Regulatory filings |
Requirement: Users must know when they're interacting with AI.
| Context | Disclosure Method | |---------|-------------------| | Chat interface | Clear "AI" label | | Voice interface | Audio disclosure | | Generated content | Watermark/label | | Automated decisions | Explicit notice |
Requirement: Communicate what the AI can and cannot do.
| Element | Disclosure | |---------|------------| | Intended use | Primary use cases | | Limitations | Known weaknesses | | Not suitable for | Inappropriate uses | | Accuracy expectations | Performance levels |
Every model must have a model card containing:
| Section | Required Contents | |---------|-------------------| | Model details | Name, version, type, architecture | | Intended use | Primary uses, users, out-of-scope uses | | Training data | Data sources, composition, limitations | | Performance | Benchmark results, evaluation methodology | | Limitations | Known limitations, failure modes | | Ethical considerations | Bias, risks, mitigations |
| Element | Disclosure Level | |---------|------------------| | Data sources | Named sources where possible | | Data composition | Categories, proportions | | Data processing | Filtering, cleaning methods | | Data limitations | Known gaps, biases |
| Content Type | Labeling Requirement | |--------------|---------------------| | Text | AI-generated indicator | | Images | Watermark + metadata | | Audio | Audio watermark + metadata | | Video | Visual indicator + metadata |
When appropriate, indicate confidence:
| Level | Description | Audience | |-------|-------------|----------| | Functional | What the AI does | End users | | Behavioral | Why the AI responded this way | Users, developers | | Technical | How the AI works internally | Experts, auditors |
"Why X instead of Y?"
| Use Case | Approach | |----------|----------| | Classification | Why this class, not another | | Generation | Why this response, not alternative | | Recommendation | Why this item, not others |
"What would change the outcome?"
| Use Case | Approach | |----------|----------| | Decisions | What input changes would change result | | Refusals | What would make request acceptable |
"What influenced this output?"
| Use Case | Approach | |----------|----------| | Text | Highlight influential words/phrases | | Images | Show attention/saliency maps | | Structured | Show feature importance |
| Context | Explanation Type | |---------|------------------| | Content refusal | Reason for refusal | | Uncertain response | Confidence indication | | Sourced claims | Citation/reference | | Recommendations | Relevance factors |
| API Feature | Purpose | |-------------|---------| | Logprobs | Token probability information | | Confidence scores | Output certainty | | Reasoning traces | Chain-of-thought (where applicable) |
| Artifact | Contents | |----------|----------| | Training logs | Training process documentation | | Evaluation results | Detailed benchmark performance | | Decision logs | Sample decision explanations | | Attention analysis | Model attention patterns |
| Document | Contents | Update Frequency | |----------|----------|------------------| | Product page | Capabilities, use cases | As features change | | Help center | How to use, limitations | Continuous | | Blog/announcements | Major updates, changes | As needed | | Research papers | Technical details | On publication |
| Document | Contents | Audience | |----------|----------|----------| | API documentation | Endpoints, parameters, examples | Developers | | Model card | Model details, performance, limitations | All | | System card | System-level information | Auditors | | Safety documentation | Safety measures, testing | Regulators |
| Document | Contents | Access | |----------|----------|--------| | Training documentation | Data, process, decisions | Internal + audit | | Risk assessments | Identified risks, mitigations | Internal | | Incident reports | Safety incidents, responses | Internal + regulators |
| Audit Type | Frequency | Scope | |------------|-----------|-------| | Documentation review | Monthly | Accuracy, completeness | | Disclosure compliance | Quarterly | All disclosure requirements | | Explanation quality | Quarterly | User understanding |
| Audit Type | Frequency | Auditor | |------------|-----------|---------| | Model audit | Annual | Third-party ML experts | | Documentation audit | Annual | Compliance experts | | User understanding study | Biennial | Research partners |
| Method | Purpose | |--------|---------| | User surveys | Verify understanding of disclosures | | A/B testing | Test explanation effectiveness | | Expert review | Technical accuracy verification | | Red team | Attempt to find undisclosed capabilities |
When AI influences significant decisions:
| Requirement | Implementation | |-------------|----------------| | Explicit AI role | Clear statement of AI's role | | Human oversight | Indication of human review | | Appeal process | How to contest decisions | | Detailed explanation | Factors that influenced outcome |
For AI-generated content:
| Requirement | Implementation | |-------------|----------------| | Labeling | Clear AI-generated indicator | | Watermarking | Technical watermark in media | | Provenance | Metadata about creation | | Detection tools | Tools to verify AI origin |
During model development:
| Requirement | Implementation | |-------------|----------------| | Internal documentation | Track decisions, data, methods | | Reproducibility | Enable result reproduction | | Version control | Track model versions | | Change documentation | Document capability changes |
| Role | Responsibility | |------|----------------| | Transparency Lead | Day-to-day compliance | | Communications Team | Public-facing content | | Legal Team | Regulatory compliance | | ESG Committee | Policy oversight |
| Activity | Frequency | Participants | |----------|-----------|--------------| | Disclosure review | Monthly | Transparency Lead | | Documentation audit | Quarterly | Cross-functional | | Policy review | Annual | ESG Committee |
| Issue | Escalation Path | |-------|-----------------| | Disclosure gap | Transparency Lead → Product Lead | | Misleading content | Communications → Legal → ESG Committee | | Regulatory concern | Legal → Board |
| Version | Date | Changes | |---------|------|---------| | 1.0 | 2025-12-17 | Initial draft |
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