CONCEPT
Condorcet's Jury Theorem
The 1785 mathematical result — now literally running inside modern AI ensemble systems — that proves a group of independent, informed judges converges on truth as it grows, and amplifies error in the same way when conditions fail.
Condorcet proved that if each member of a group has a probability greater than one-half of making a correct decision on a binary question, the probability that the majority decision is correct increases with group size, approaching certainty. The theorem has a dark mirror: if individual reliability is below chance, larger groups converge on error with the same mathematical certainty. The theorem is therefore a double-edged sword — a justification for inclusive governance under specific conditions, and a diagnostic of how collective decision-making can systematically amplify mistake. Modern machine learning ensembles — random forests, boosting algorithms, voting classifiers — operate on the same mathematical structure. The theorem is not a metaphor for AI ensembles; it is the literal foundation beneath them.
In The You On AI Field Guide
The theorem has two critical conditions: individual reliability greater than chance, and independence of errors among participants. The first condition is what makes universal education a