NEAR Protocol: Making AI Transparent and Verifiable

Artificial Intelligence (AI) has brought major breakthroughs, changing how we work, learn, and interact with technology. But as AI becomes more advanced, so do the concerns around trust, transparency, and control. Most people rely on closed systems run by big companies, with little insight into how decisions are made or how personal data is handled. That lack of visibility raises important questions about accuracy, accountability, and privacy.

NEAR Protocol takes a different path – one that combines AI with the transparency of blockchain. The result is AI that’s not only powerful but also open, auditable, and built around user control. Let’s take a closer look at how NEAR is making this possible.

Why Verifiable AI Matters

Many of today’s most popular AI tools – like GPT-4 or Claude – work like black boxes. You give them a prompt, and they give you an answer, but you can’t see how they reached that conclusion. You don’t know what data they used, how it was processed, or whether you can trust the result. This is where the concept of verifiable AI comes in: systems that are open about how they work and that allow anyone to check and confirm their outputs.

NEAR’s mission is to build AI that users can trust – AI that’s open by design, and where both processes and results can be independently verified.

Building Transparent AI on NEAR

A key part of this effort is NEAR’s decentralized AI hub, available at NEAR.ai. Here, every AI agent is tied to a NEAR account. This makes every interaction traceable, and every decision open to inspection.

Take Learn NEAR Club’s AI assistant, for example – called (L)Earn AI🕺. It doesn’t just give answers; it shows how it works. Users can check its source code, prompts, filters, and the exact model it’s running. Nothing is hidden. That level of openness allows people to trust the tool – or even customize it to better suit their needs.

NEAR also supports open-source AI models, meaning anyone can verify which model an agent is using and ensure that it hasn’t been secretly changed.

To get more details on how transparent and verifiable AI works on NEAR please refer to this Guide

Example: Fact-Check AI at Learn NEAR Club

One standout use case is the Fact-Check Agent developed by Learn NEAR Club (LNC). This tool helps users fact-check claims – like those found on social media – by tapping into a vetted knowledge base backed by NEAR.

Here’s how it works:

• User Input: You submit a claim and where it came from

• Verification: The agent uses RAG (Retrieval-Augmented Generation) to look up trustworthy information from the LNC database powered by XTrace

• Result: It responds with a clear judgment (valid or invalid), backed by references (coming soon)

• Feedback: You can agree, flag a mistake, or suggest improvements – earning nLEARN tokens when you help identify missing info. This creates a cycle where the AI gets smarter and the community helps keep it accountable.
  You saw a post on Z and you want to make it sure it is legit to interact with.

   
  Paste the fact and source URL into the agent

  
  AI agent responded:
  
  Cool, seems about right!
  Now let’s protect ourselves and make (L)Earn AI responsible for the response!

nStamp: Proof on the Blockchain

To make things even more verifiable, NEAR offers nStamp – a way to anchor AI outputs on-chain. When an agent gives an answer, you can “stamp” it to the blockchain.
This creates a hash of trace, along with a timestamp. The trace contains details of the actual request sent to the agent, along with the original response and HTTP status code.

Anyone can verify the origin account, timestamp and the hash at NEAR explorer of their choice.

Now let’s check if nStamp actually did what it promised to do! Let’s verify the nstamped data:

Copy and paste the AI interaction trace into verification tool:

Hashes matched – we are all good!

Later, anyone can check that stamp to confirm the output hasn’t been changed. If the data matches, the result is proven authentic – right down to the second it was verified.
If any actor – user, agent or model tries to tweak the data for some reason – verification fails.

Why It Matters

In an online world filled with misinformation, these tools make a real difference. Whether you’re a student, researcher, or just someone trying to check a viral post, NEAR gives you the power to validate facts quickly – and trust the results. This “verifiable AI” model puts users first by making every step in the process transparent.

Karpathy’s Rule: “Keep the AI on a tight leash.”

At YC’s AI Startup School in June 2025, AI expert Andrej Karpathy said it best: “Keep the AI on a tight leash.” He argued that real-world AI products need constant verification – not just impressive demos. For him, a reliable system is one where every answer is paired with a fast check, often by a second model or human.

NEAR’s approach follows that advice to the letter. With LNC’s Fact-Check and nStamp, every AI-generated result gets a follow-up check and a chance to be locked into the blockchain. That’s the “generation-verification loop” in action.

Try It for Yourself

This isn’t a future concept -it’s live now. You can try the Fact-Check AI at LNC website, submit a claim, verify a result, and stamp it on-chain. It’s a hands-on way to see how blockchain and AI can work together to build trust – one verified fact at a time.

Updated: July 28, 2025