VAI

VAI is an AI chatbot that brings transparency and accountability to large language model interactions by issuing VCs that show the provenance of the inputs, outputs, and the specific model being used.

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Inspiration

The rising concern about AI-generated content authenticity and the lack of transparency in AI interactions drove us to create VAI. With the proliferation of AI chatbots and growing instances of AI-generated misinformation, we recognized the critical need for a system that could provide verifiable proof of AI interactions.

What it does

VAI transforms traditional AI interactions into verifiable exchanges by:

  • Creating digital credentials for each conversation that capture input prompts and output responses
  • Generating cryptographic proofs of the specific model version used
  • Maintaining an immutable audit trail of all interactions
  • Providing a user-friendly interface to verify the authenticity of AI-generated content
  • Enabling users to trace the lineage of any AI-generated response back to its source

How we built it

We developed VAI using a combination of:

  • A front-end interface built with React for user interactions
  • A backend system using Node.js and Express
  • Integration with various LLM APIs to handle AI conversations
  • A custom credential issuance system using digital signatures
  • Cheqd DID Linked Resources for publishing public conversations

Challenges we ran into

  • Including the previous conversation in a new message
  • Implementing a scalable system for credential generation without compromising performance
  • Balancing user privacy with transparency requirements
  • Developing a standardized format for credential verification across different LLM platforms
  • Creating a user experience that maintains simplicity while providing comprehensive verification

Accomplishments that we're proud of

  • Successfully created a working prototype that demonstrates end-to-end verification
  • Developed a novel approach to AI interaction authentication
  • Built a scalable architecture that can handle multiple LLM integrations
  • Achieved near-real-time verification without significant latency
  • Created an intuitive user interface that makes verification accessible to non-technical users
  • Established a foundation for industry-wide AI verification standards

What we learned

  • The complexity of implementing cryptographic proof systems at scale
  • The importance of balancing security with usability
  • Technical challenges in standardizing verification across different AI models
  • The critical role of user experience in adoption of security features
  • The potential impact of verification systems on AI trust and accountability
  • The growing demand for transparency in AI interactions

What's next for VAI

Our roadmap includes:

  • Expanding support for additional language models and AI platforms
  • Implementing advanced verification features using zero-knowledge proofs
  • Developing plugins for popular messaging platforms and browsers
  • Creating an open API for third-party developers
  • Establishing partnerships with major AI providers
  • Working towards becoming an industry standard for AI interaction verification
  • Exploring integration with existing digital identity systems

The future of VAI lies in creating a comprehensive ecosystem where verified AI interactions become the norm rather than the exception, fostering trust and accountability in the AI space.

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