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The following is a guest post from Felix Xu, Founder of ARPA Network.

The U.S. government’s approach to artificial intelligence (AI) has shifted dramatically, emphasizing accelerated innovation over regulatory oversight. In particular, President Donald Trump’s executive order, Removing Barriers to American Leadership in Artificial Intelligence, has set a new tone for AI development, one rooted in promoting free speech and advancing technological progress. Similarly, U.S. Vice President JD Vance’s refusal to endorse a global AI safety agreement signals that America will prioritize innovation without compromising on its competitive advantage.

However, as AI systems increasingly become more influential in financial markets, critical infrastructure, and public discourse, the question remains: How can we ensure trust and reliability in AI model-driven decisions and outputs without stifling innovation?

This is where Verifiable AI comes in, offering a transparent, cryptographically secure approach to AI that ensures accountability without heavy-handed regulation.

AI’s rapid advancement has ushered in a new era of intelligent AI agents capable of complex and autonomous decision-making. But without transparency, these systems can become unpredictable and unaccountable.

For instance, financial AI agents, which rely on sophisticated machine learning models to analyze vast datasets, are now operating under fewer disclosure requirements. While this encourages innovation, it also raises a trust gap: without insight into how these AI agents reach their conclusions, companies and users may struggle to verify their accuracy and reliability.

A market crash triggered by an AI model’s flawed decision-making is not just a theoretical risk, it’s a possibility if AI models are deployed without verifiable safeguards. The challenge is not about slowing down AI progress but ensuring that its outputs can be proven, validated, and trusted.

As renowned Harvard psychologist B.F. Skinner once said, “The real problem is not whether machines think but whether men do.” In AI, the key issue is not just how intelligent these systems are, but how humans can verify and trust their intelligence.

Russel Wald, executive director at the Stanford Institute for Human-Centered Artificial Intelligence, sums up the U.S. AI approach:

“Safety is not going to be the primary focus, but instead, it’s going to be accelerated innovation and the belief that technology is an opportunity.”

This is precisely why Verifiable AI is crucial. It enables AI innovation without compromising trust, ensuring AI outputs can be validated in a decentralized and privacy-preserving way.

Verifiable AI leverages cryptographic techniques like Zero-Knowledge Proofs (ZKPs) and Zero-Knowledge Machine Learning (ZKML) to provide users with confidence in AI decisions without exposing proprietary data.

In essence, Verifiable AI provides an independent verification layer, ensuring that AI systems remain transparent, accountable, and probably accurate.

America’s AI trajectory is set for aggressive innovation. But rather than relying solely on government oversight, the industry must champion technological solutions that ensure both progress and trust.

Some companies may take advantage of looser AI regulations to launch products without adequate safety checks. However, Verifiable AI offers a powerful alternative empowering organizations and individuals to build AI systems that are provable, reliable, and resistant to misuse.

In a world where AI is making increasingly consequential decisions, the solution is not to slow down progress, it’s to make AI verifiable. That’s the key to ensuring AI remains a force for innovation, trust, and long-term global impact.

The post Verifiable AI: The key to balancing innovation and trust in AI policy appeared first on CryptoSlate.

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