AI Will Transform Everything But First It Needs a Trust Layer

Artificial Intelligence stands at a pivotal moment. Its potential to transform every industry it touches and to enhance our personal lives is undeniable. No one needs convincing of that. From predictive healthcare to personalised assistants, AI is reshaping how we interact with the world.

Yet, for all its promise, AI faces significant hurdles and chief among them is trust. No one needs convincing of that either. Every new technology encounters challenges, not just in terms of code, but also in terms of culture. How should we interact with it? Will it change us for the better or do we first have to change it for the better?

It’s easy to see how what starts out as a technical question can quickly evolve into an ethical one. And when it comes to ethics, AI is a hot mess.

 

Trust or Bust

 

Our world thrives on trust. It is this essential quality that enabled humans to evolve from primitive tribes into highly organised societies capable of trading with one another, passing through one another’s lands, and sharing discoveries. In the digital age, we’ve gone further and developed systems that can establish trust on behalf of humans, that’s what a blockchain is after all but when it comes to AI, the concept of trust becomes a lot fuzzier.

It’s perhaps no coincidence that artificial intelligence, which blurs the lines between human and machine, finds itself stuck in no-man’s land when it comes to trust. On the one hand, AI is bound to follow the coded instructions it’s given to the letter.

But at the same time, it’s expected to perform its duties in a very human-like fashion, those same humans who are susceptible to lying, cheating, and plagiarising one another. If we can’t trust our AIs, it’s because we can’t trust ourselves.

Human shortcomings are hard to fix; we’ve been grappling with them for millions of years and are still as error-prone and emotional as our ancestors but AI should be easier to fix. Because we already have the technology to establish trust in a trustless setting (yep, we’re back to blockchain again) but it’s yet to be widely implemented in the context of AI.

Without a robust framework to ensure ethical data use and transparency, AI risks falling short of its transformative potential. To solve this, it needs an additional layer, one that’s dedicated to trust.

Don’t Trust, Verify

 

“Don’t trust, verify” is a popular saying among bitcoiners attesting to blockchain’s ability to serve as an independent arbiter of truth, a verification layer that can irrefutably establish events that have occurred. A timestamp. A transaction. A transfer. It’s all indelibly recorded on public blockchains for anyone to inspect and verify.

Now imagine what would happen if we applied that capability to AI. The days of relying on opaque training models, closed algorithms and dubiously scraped data would be over. It would put an end to the current era of AI whose own saying might as well be “Trust me bro.” When we don’t know how our AI was trained, where it’s getting its data from, and which information it’s been instructed to direct to us and which it’s ordered to divert, we’re operating in the dark.

So what might a trust layer for artificial intelligence resemble in practical terms? To see how such a solution actually plays out, consider Vyvo, whose “Life CoPilot,” effectively an AI operating system for healthcare wearables comes with a built-in trust layer.

This is achieved using the Proof-of-Sensing (PoSe) validation protocol Vyvo has developed. Vyvo is also currently gearing up for its token launch that will see VAI tokens issued to the public to expand its vision of a blockchain-based smart economy.

The PoSe protocol provides a secure and safe reward system that addresses the challenges of data provenance, validation, and consistency. It facilitates complex auditing processes and prevents the system from being impacted by malicious actors attempting to manipulate the data.

The PoSe validation protocol has been designed for the digital health-sharing economy but the same principle can be applied to all industries AI intersects with, which is pretty much all of them.

The implications of establishing an AI trust layer extend beyond wearables. Setting a standard for trusted data counters the “black box” problem, where AI outputs are opaque, by making data provenance clear. It also mitigates bias by prioritising high-quality, real-world inputs over scraped datasets. And it empowers users, giving them agency in an era where data is often exploited.

 

Why Ethical AI Matters 

 

AI’s capabilities are vast. It can analyse massive datasets to predict disease outbreaks, optimise supply chains, and tailor educational experiences to individual learners.

But the current crop of AI systems rely on scraped or unverified data, often collected without explicit user consent, raising ethical concerns about privacy and data ownership. Until they’re fixed, trust in AI’s integrity is impossible. How can it drive our cars and teach our kids if we have no insights into its actions?

From user data being repurposed without consent to biased data leading to inaccurate outputs, perpetuating errors or discrimination, it all circles back to trust.

As governments introduce stricter AI regulations like the EU’s AI Act, systems that lack transparency or accountability risk obsolescence. For AI to reach its full potential, it must operate on a foundation of reliable, consented data that respects user autonomy. In the absence of this, even the most advanced algorithms will struggle to deliver ethical outcomes. 

AI’s potential is boundless, but it hinges on trust. Humans have already proven that they can create AI that’s smart. Now they need to prove they can develop AI that is trustworthy. Achieve that, and we’ll have created super intelligence that inherits all of our best traits and none of our worst.