Human Data Admissibility Layer

Most human data systems rely on post-hoc quality assurance.

This creates a structural problem: invalid data can be created freely, and must be detected later.

This repo demonstrates a different approach:

→ Prevent invalid data states from being created at all
→ Enforce admissibility at the point of contribution

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Why this matters

In AI systems, human-generated data directly shapes model behavior.

If invalid states are allowed at the point of creation, they propagate into training, evaluation, and alignment layers.

This turns data quality into a probabilistic outcome.

This repo treats data quality as a system property instead.

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Architecture

Contributor → Constraint Layer → Accepted / Rejected → Downstream Systems

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What this repo demonstrates

A minimal “Human Data Admissibility Layer”:

1. Define inadmissible states (not all valid states)
2. Enforce constraints at submission time
3. Reject invalid inputs immediately
4. Track system behavior via rejection telemetry

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Example

Run:

python run_demo.py

Output:

Valid: False, Reason: low_confidence_submission

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Takeaway

If invalid states cannot be created,
data quality becomes a property of the system — not the reviewer.