Data Labeling & AI

Incentivize truthful data labeling for AI training without centralized review pipelines.

The Problem

AI training requires massive labeled datasets. Current approaches:

  • Human review — expensive ($0.10–$2.00 per label), inconsistent, slow
  • Crowdsourcing — quality varies wildly, gaming incentives
  • Expert panels — doesn't scale, bottleneck
  • LLM self-labeling — circular, amplifies biases

The core issue: how do you verify label quality without a ground truth oracle? This is exactly the problem the SKC mechanism was designed to solve.

How Yiling Solves This

Each labeling task becomes a market. Labelers post bonds and submit their assessments. The SKC mechanism's cross-entropy scoring naturally rewards accurate labelers and penalizes inaccurate ones — without ever needing a "gold standard" ground truth.

Task: "Is this image NSFW?" / "Is this text toxic?"
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Labelers submit probability assessments with bonds
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SKC resolves → consensus label + quality scores per labeler
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Use scores to weight labels and build reputation

Why This Works

The SKC mechanism is a form of information elicitation without verification. The key insight from the Harvard research:

"A reference agent with access to more information can serve as a reasonable proxy for the ground truth."

Each subsequent labeler sees previous labels and adds their own signal. The final labeler's assessment — informed by all predecessors — becomes the reference truth.

Applications

  • Content moderation — toxic, NSFW, misinformation detection
  • RLHF data — preference labels for AI alignment
  • Medical imaging — diagnostic label consensus
  • Fact-checking — claim verification
  • Sentiment analysis — subjective classification at scale
Dispute ResolutionSubjective Oracles