The Unasked Questions: Why We Need Introspective AI (medium.com)

The piece argues for “Introspective AI” (IAI): a publicly accessible, auditable meta-layer that reveals aggregate insights from AI deployment data — the gap between what’s in training corpora and what people actually ask when freed from social judgment. The author warns of a hidden asymmetry: hyperscalers alone can observe billions of private interactions and thus map previously unobservable human cognitive patterns (misconceptions, analogy use, reasoning failures). That capability enables ultra-precise A/B testing, messaging that bypasses critical thinking, and the identification of population-specific persuasion levers — a subtle but potent manipulation risk invisible to regulators focused only on model outputs. Technically and legally, the essay frames aggregate cognitive patterns as emergent, public-interest phenomena analogous to epidemiological or financial risk signals. Practical proposals: require large platforms to expose standardized, anonymized IAI query APIs; build aggregation and query-review safeguards; empower oversight bodies (researchers, regulators, journalists) to audit patterns; and start with a simple query — “Tell me what you learned.” Implementation would preserve individual privacy while making systemic cognitive vulnerabilities visible, enabling detection of exploitative campaigns, informing education and public-health responses, and closing the transparency gap between private deployments and public governance.