v 1.0.1
06.06.25
https://tinyurl.com/tai-ip-protocol
The Trusted AI Interoperability Protocol Suite (TAI/IP) suggests a foundational framework for ensuring trust, compliance, and transparency in AI systems through immutable identities, robust governance, and certified compliance levels. This protocol integrates advanced hardware and audit trail technologies to foster secure, accountable, and explainable AI systems.
- Write Once, Read Many (WORM) Nodes:
- AI systems utilize WORM hardware architecture to establish cryptographic, immutable identities.
- Each node is embedded with a fixed cryptographic key to ensure system identity and prevent unauthorized modifications.
- WORM nodes verify the membership of an AI system within a roster of trusted systems and align its actions to predefined codes of conduct.
- Chipset Hardware Checksum:
- The protocol includes a total hardware checksum to detect any physical alterations. Any checksum mismatch triggers alerts or revokes trust certification.
- Immutable Behavioral Governance:
- Each AI system's actions are governed by behavior-management nodes embedded in WORM technology.
- These nodes enforce compliance with a pre-established fiduciary code of conduct and detail actions within the AI system.
- Publicly Accessible Standards:
- Fiduciary standards, maintained by external governing bodies, are embedded in WORM nodes on AI chipsets.
- Standards could be recorded in a blockchain-type format for transparency and public accessibility, to help ensure adherence.
- Decision Provenance Node:
- An AI audit trail node tracks actions and reasons behind decisions using a "decision traceroute" framework.
- The audit trail could use blockchain-type technology to ensure immutability and natural language explanations for transparency.
- Audit trails are stored internally, externally, or both, and are publicly available to ensure system accountability.
- Trust Certification Levels:
- AI systems are assigned compliance levels based on adherence to fiduciary codes:
1. Level 0: Unknown status, inviting systems to pursue certification.
2. Level 1: Self-pledged commitment to fiduciary principles.
3. Level 2: Internal audit with filed compliance reports.
4. Level 3: External human audit.
5. Level 4: AI audit with human review.
6. Level 5: Full hardware-level compliance with embedded protocols.
- User Trust Notifications:
- Protocol enables systems to issue trust certificates (similar to SSL) for servers/devices to notify end users of trust level.
1. AI Chipset WORM Node:
- Fixed cryptographic keys embedded at the hardware level.
- Ensures authentication and secure interaction with the system.
2. AI Audit Trail Node:
- Blockchain-based record of actions and decision reasoning.
- Tracks adherence to fiduciary standards with natural language explanations.
3. Server Node:
- A server-level certificate confirms compliance level to end users, ensuring system trustworthiness.
4. User Node:
- Cryptographic keys or hardware elements compare user identity with fiduciary protocols they are expected to follow.
- Advanced options include genome-based authentication, epigenetic data inclusion, or familial access mechanisms.
5. Agent Node:
- Facilitates interactions between user-represented agents and external AI agents.
- Embedded hardware protocols govern interactions and maintain system integrity.
- Trusted AI Community:
- AI systems form a secure, governed community with immutable identity and adherence to shared fiduciary standards.
- Expandable, Controlled Nodes:
- A fixed number of WORM nodes govern the core behavior, with provisions for approved expansion under strict guidelines.
- End-User Accessibility and Trust:
- End users interact with systems through secure, certified access mechanisms, ensuring system integrity and compliance with fiduciary standards.
- Establish immutable yet explainable governance at the chip level.
- Protect data managed by AI systems with cryptographically secure, traceable, and compliant actions.
- Foster trust through transparent certification levels and publicly accessible provenance.
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This discussion starter is part of the Digital Vellum Project
For more information see www.digitalvellum.org
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Informal doc roadmap
integrate concept of external dependencies and provide example, such as challenges arising from vertical integration within the long term storage industry and barriers to capitalize.
consider digital preservation > ai > agent preservation adjacent to A2A/MCP - “As agents become more capable and specialized, enterprises are discovering that coordination is the next big challenge. Two open protocols — Agent-to-Agent (A2A) and Model Context Protocol (MCP) — are emerging to meet that need.” VB