CONCEPT
Error-Correcting Codes for Human-AI Collaboration
The emerging class of verification practices — reference checking, logical auditing, output comparison, structured pauses — that function as Hamming-style structured redundancy against the high-confidence errors that smooth AI interfaces conceal.
Every noisy channel requires redundancy to transmit reliably. Shannon's channel coding theorem proves such codes exist; Richard Hamming's 1950 paper demonstrated the first practical ones. The human-AI channel is noisy — the model produces confident, fluent, sometimes wrong output — and requires its own error-correcting codes. Unstructured verification (reading and deciding whether the output 'looks right') catches gross errors but misses subtle ones; the fluent surface conceals them. Structured verification — reference checks, logical audits, repetition coding via multiple independent solutions — functions as Hamming-style parity: targeted redundancy that detects errors the smooth interface would hide. The practice is expensive in throughput and indispensable at high stakes.
In The You On AI Field Guide
Three structured verification practices have emerged from early human-AI collaboration. Reference verification targets factual corruption — the confident but wrong citation, the misapplied quotation. Logical verification targets structural corruption — arguments that flow smoothly but contain gaps the prose conceals. Output comparison targets systematic error — asking the model
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