AIPS DPP Specification Downloads
Purpose
This page provides direct download links to all machine-readable and human-readable artifacts that define the AIPS DPP Profile (v0.1).
The AIPS DPP Profile extends the BPS DPP Core to describe AI Products with additional model, evaluation, and risk metadata, while preserving compatibility with the base DPP verification and trust framework.
All artifacts listed below are publicly accessible under the versioned namespace:
[https://kivanura.org/spec/aips/dpp/0.1/](https://kivanura.org/spec/aips/dpp/0.1/)
1. Machine-Readable Artifacts
| File | Type | Purpose |
|---|---|---|
aips-dpp-0.1.context.jsonld | JSON-LD Context | Defines AI-specific DPP vocabulary (e.g., modelCard, eval, risk, policy). |
aips-dpp-0.1.schema.json | JSON Schema | Machine-validated structure for AIPS DPP instances. |
aips-dpp-0.1.shacl.ttl | SHACL Shapes | Semantic validation rules compatible with RDF/Linked Data tools. |
aips-dpp-0.1.ttl | RDF Vocabulary | Ontological representation of the AIPS DPP Profile. |
aips-dpp-0.1.openapi.yaml | OpenAPI Contract | Defines API endpoints for retrieving Lite and Full AIPS DPPs. |
Each of these files aligns structurally with the corresponding BPS DPP Core 0.1 artifacts and can be validated or combined programmatically.
2. Human-Readable Guides
| File | Type | Description |
|---|---|---|
aips-dpp-overview.md | Markdown | Overview of scope, composition, and relationship with BPS Core. |
aips-dpp-implementation-guide.md | Markdown | Step-by-step guidance for producers issuing AI Product DPPs. |
aips-dpp-consumers-guide.md | Markdown | Guidance for consumers on reading, validating, and verifying AIPS DPPs. |
aips-dpp-examples.md | Markdown | Realistic Lite and Full DPP examples for reference. |
These documents complement the machine-readable files by describing usage, validation, and implementation best practices.
3. Example Instances
| File | Type | Description |
|---|---|---|
fraud-detector-lite.example.jsonld | Example Instance | AIPS Lite Passport for a FraudDetector AI model. |
fraud-detector-full.example.jsonld | Example Instance | AIPS Full Passport for the same model, including ByRef sections. |
vision-classifier-lite.example.jsonld | Example Instance | Compact DPP for an image-classification AI product. |
All example instances are syntactically valid according to the AIPS DPP schema and SHACL constraints.
4. Alignment and References
| File | Purpose |
|---|---|
alignment.md | Describes semantic alignment between AIPS DPP and BPS DPP Core vocabularies. |
security-privacy.md | Outlines redaction, privacy, and signature-handling expectations. |
versioning-policy.md | Explains the version management model and lifecycle semantics. |
CHANGELOG.md | Tracks version history, updates, and backward-compatibility notes. |
5. Namespace Summary
| Prefix | IRI |
|---|---|
aipsdpp: | https://kivanura.org/spec/aips/dpp/0.1/ |
bpsdpp: | https://kivanura.org/spec/bps/dpp/0.1/ |
xsd: | http://www.w3.org/2001/XMLSchema# |
rdf: | http://www.w3.org/1999/02/22-rdf-syntax-ns# |
sh: | http://www.w3.org/ns/shacl# |
Namespaces ensure that every AIPS DPP instance can interoperate with the broader BPS ecosystem and RDF/Linked Data tooling.
6. Usage and Validation Notes
-
All JSON-LD and Turtle artifacts can be validated using:
pyldorrdf-validate-shaclfor semantic checks.ajvor similar libraries for JSON Schema compliance.
-
Producers must include both contexts in AIPS DPP instances:
"@context": [
"https://kivanura.org/spec/bps/dpp/0.1/bps-dpp-core-0.1.context.jsonld",
"https://kivanura.org/spec/aips/dpp/0.1/aips-dpp-0.1.context.jsonld"
]
* Consumers should verify signatures and content hashes per the BPS DPP Core validation process.
---
## 7. Version and Governance
* **Version:** 0.1 (Initial Release)
* **Namespace:** `https://kivanura.org/spec/aips/dpp/0.1/`
* **Maintainer:** Kivanura Foundation
* **License:** CC BY-SA 4.0
* **Change Policy:** Minor versions maintain backward compatibility; major versions introduce schema or vocabulary extensions.
Future versions (e.g., `0.2`, `1.0`) will remain linked under the parent path:
```
https://kivanura.org/spec/aips/dpp/
```
---
## 8. Summary
The AIPS DPP Profile provides a structured, verifiable, and interoperable mechanism for representing the **digital trust layer of AI Products**.
By combining the BPS DPP Core with domain-specific extensions, it enables transparent model governance and reproducibility across the entire AI product lifecycle.
---