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With the arrival and explosion of artificial intelligence systems, the Turing Test has become obsolete. AI can mimic human speech, solve intricate problems and charm us with its fluency. But intelligence isn’t just about sounding human—it’s about knowing when you’re wrong.
Enter the Socrates Test: What matters is not whether AI can pass as human, but whether it has the wisdom to recognize its own limits. The test of real intelligence isn’t eloquence; it’s epistemic humility.
Why AI Can’t Escape Error
AI’s flaws aren’t a glitch—they’re structural. AI can’t escape errors in the same way that humans might. To illustrate this argument, researchers Alexander Bastounis, Paolo Campodonico, Mihaela van der Schaar, Ben Adcock and Anders C. Hansen pinpoint [ [link removed] ] the Consistent Reasoning Paradox—a mathematical trap that ensures AI hallucinations.
Here’s the thinking behind the paradox: AI is designed to provide consistent answers to equivalent questions. That’s useful—humans naturally recognize that “What time is it?” and “Tell me the time!” should yield the same response. AI is trained to mimic this pattern.
But consistency becomes a curse when AI is required to answer everything. When “I don’t know” isn’t an option, it has no choice but to generate plausible-sounding responses—even when the truth is unknown. That’s the trap: fluency without verification. The researchers prove it—an AI that never refuses to answer a question or a problem posed to it will often hallucinate.
A cautious AI that declines to provide an answer when it’s uncertain makes fewer mistakes. But a relentless answer machine? It’s doomed to err.
For instance, ask an AI to optimize chemotherapy—minimize days while delivering a set dosage. With decimals (e.g., 2.5 mg/day), AI excels. Switch to fractions (5/2 mg/day), and it delivers a conflicting plan. AI stumbles on fractions versus decimals because it does not inherently grasp equivalence—it processes symbols rather than concepts. When faced with a mathematical problem expressed differently—such as 2.5 mg/day versus 5/2 mg/day—it sees distinct inputs rather than equivalent ones. Unlike humans, who recognize these as the same, AI treats them as separate cases, potentially generating conflicting outputs.
But AI’s greatest flaw isn’t just that it makes mistakes; it’s that it fails to recognize them. Ask AI to check its past answers. Feed it a prior response and say, “Was this right?” Sometimes, it confidently confirms a mistake. Other times, it rejects a correct answer.
Self-awareness is the foundation of human reasoning—we recognize when we don’t know. But AI lacks that fail-safe.
Optimal Hallucinations: A Balancing Act
So if AI is prone to error, what should we do? The naive answer is to eliminate such hallucinations. But that’s a mistake because, while uncontrolled hallucinations are dangerous, selective hallucinations are essential. The trick isn’t stopping them—it’s knowing when to allow them.
In casual chats, hallucinations are fine, even fun, because creativity, not accuracy, is the goal: Ask a large language model for a peach cobbler recipe in iambic pentameter, limited to ingredients in Henry VIII’s pantry, and you will get an answer. In that case, “accuracy” is a nonsensical concept.
But in medicine, law or engineering—where mistakes cost lives or fortunes—hallucinations aren’t quirks; they’re liabilities. The ideal AI adapts: playful when it’s safe, precise when it’s not.
We’ve built structured, robust hybrid models for high-stakes fields like mine—legal—where we’ve solved the Socrates Test. Consumer bots, though? They’re still improvising bards, and that’s okay.
The Bigger Mirror: AI and Us
But here’s the deeper problem: AI’s failures mirror our own.
We claim to prize doubt and want an AI that will pass the Socrates Test, but do we? In public life, we don’t cheer the uncertain—we amplify the loud and absolute. Social media thrives on certainty, not nuance. Socrates questioned dogma; today, he’d be drowned out by retweets.
This isn’t a tech problem—it’s a social one. Our systems—clicks, likes, shares—reward bravado over humility. Yet technology could flip this.
We built AI to imitate us. But perhaps, paradoxically, we can learn from it instead. An AI that models doubt—pausing, weighing, admitting uncertainty—could force us to rethink our own habits of overconfidence. The real challenge isn’t just designing better AI. It’s designing a society that values uncertainty as much as truth.
The real challenge isn’t just smarter AI. It’s smarter norms that reward wisdom, not noise. But this innovation is a matter of social technology, not silicon technology. That’s not just engineering intelligence—it’s engineering a better us.

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