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What is Artificial Wisdom?

Artificial intelligence is usually judged by what it can do. Artificial wisdom asks a harder question: can a system make sound judgments when the answer is uncertain, human and complicated?

Smart is not the same as wise.

A system can be very capable and still miss context, overstate its confidence or optimize the wrong goal. In 2020, psychiatrist Dilip Jeste and colleagues argued that useful technology should go beyond intelligence. Their proposed AW systems would learn from experience, correct themselves, act compassionately and ethically, and help people make wiser decisions. They also called for computer scientists to work with experts in neuroscience, mental health and ethics.¹

A newer proposal: help AI notice its own limits.

A 2026 paper led by Samuel Johnson, Amir-Hossein Karimi, Yoshua Bengio and Igor Grossmann describes wisdom as strategies for messy problems that cannot be solved by calculation alone. The authors focus on metacognition, which simply means thinking about how you are thinking. For AI, that could include admitting uncertainty, seeking another perspective, adapting to context and knowing when a familiar rule does not fit.²

The researchers propose ways these skills might be trained and measured. They argue that better metacognition could make systems more robust, explainable, cooperative and safe. Those are research goals, not established capabilities. The work offers a roadmap, not proof that a machine is wise.³

The skeptical view matters.

Some critics question whether “artificial wisdom” gives software too much human meaning. In a 2026 letter, physician Prasan Kumar Panda argues that fluent AI output can create an appearance of certainty without understanding, especially when evidence is incomplete. He says responsibility and ethical judgment must remain with people, and suggests “epistemic stewardship”, careful human custody of knowledge, as a better frame.⁴

That criticism points to a practical danger: calling a system wise may encourage people to trust it before its claims, values and limits have been tested. A wise-sounding answer can still contain a fabricated source or a bad assumption.

Can wisdom be measured?

Not reliably yet. Researchers can test parts of the idea, such as uncertainty awareness, perspective-taking or adaptation to context. But there is no broadly accepted test proving that an AI system is wise. Human wisdom itself has several definitions, and a benchmark can reward an answer that sounds good without showing sound judgment in the real world.

What would progress look like?

• Humility: saying what it does not know.
• Perspective: considering who may be helped or harmed.
• Context: adapting instead of forcing one rule onto every situation.
• Accountability: leaving people responsible for consequential choices.
• Evidence: testing behavior, not trusting a polished explanation.