Cognitive Engines — Bayesian Confidence, Bias Detection

The Engines

• Bayesian Confidence Engine — Updates confidence scores using Bayes' theorem as new behavioral signals arrive. Prior = initial score. Likelihood = strength of new evidence. Posterior = updated score.
• Bias Detection Engine — Monitors all engine outputs for systematic disparities across demographic cohorts defined by context metadata. Flags when any engine's error rate differs significantly across groups.
• Cross-Cultural Universals Engine — Identifies behavioral patterns that are statistically consistent across cultural contexts, and flags patterns that are culturally specific. Prevents cross-cultural overgeneralization.
• Empirical Validation Engine — Compares engine predictions against ground truth outcomes (when available) and tracks calibration drift over time.

Code Walkthrough

// Bayesian confidence update
function updateConfidence(prior, newEvidence) {
  const likelihood = computeLikelihood(newEvidence);
  // Bayes: P(H|E) = P(E|H) * P(H) / P(E)
  const posterior  = (likelihood * prior) /
    ((likelihood * prior) + ((1 - likelihood) * (1 - prior)));
  return Math.min(1, Math.max(0, posterior));
}

// Bias detection: check if error rates differ across groups
function detectBias(engineId, predictions, outcomes, groupMetadata) {
  const groups   = groupBy(predictions, p => groupMetadata[p.contextId]);
  const errorRates = Object.fromEntries(
    Object.entries(groups).map(([group, preds]) => [
      group,
      preds.filter((p, i) => p.score > 0.5 !== outcomes[i]).length / preds.length,
    ])
  );
  const maxDelta = Math.max(...Object.values(errorRates)) -
                   Math.min(...Object.values(errorRates));
  return { engineId, errorRates, maxDelta, biasFlag: maxDelta > 0.1 };
}

What to Watch For

• The bias detection engine requires ground truth outcomes to function. Plan from day one how you will collect them.
• Cross-cultural universals should be validated by domain experts, not only by statistical correlation.