The Alignment Problem

The Alignment Problem

AI systems are only as good as the data they’re trained on. Yet most models today are optimized for objective proxies, not subjective human preferences.

Questions like:

• Which response feels more helpful?

• Which image better matches a prompt?

• Which answer sounds more human?

• Which outcome would you choose?

These are judgments only real humans can make — but collecting such feedback at scale has traditionally been slow, expensive, and inaccessible.

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Proof of Human Alignment (PoHA)

We call the contribution and reward protocol that powers Reppo Network Proof of Human Alignment (PoHA) — an infrastructure for generating and curating preference data to train and evaluate human-aligned AI systems.

PoHA incentivizes two key behaviors:

• Creation of AI-generated content that reflects human values, intentions, and quality standards.

• Evaluation of that content via human preference signals, such as ranking, voting, or comparative feedback.

Together, these activities generate rich, scalable preference datasets — a crucial ingredient for aligning large models and autonomous systems with what people actually want.

How PoHA Addresses the Alignment Problem

PoHA transforms this bottleneck into a scalable, incentive-driven ecosystem. By rewarding contributors who both create and evaluate aligned AI outputs, it enables the continuous generation of high-quality human preference data.

This makes it possible to train models not just to perform tasks — but to align with human intentions, values, and sense of quality.

In short, PoHA turns human judgment into a measurable, network-verified signal — the foundation of truly aligned AI.