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LeadersGoldDiscovery

What AI must learn to say I don’t know

    • Date
    9 July 2026
    Timeframe
    15:00 - 15:30
    Duration
    30 minutes
    • Days
      Hours
      Min
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    AI systems today answer every question. They do this whether the evidence is strong, weak, or missing entirely. That behavior becomes dangerous the moment these systems are used in decisions that affect people, policy, and planetary outcomes.

    More data is often treated as progress. It isn’t. Data accumulates. Information emerges only when differences are revealed and compared. Without that structure, systems scale input without deepening understanding.

    Evidence plays a very specific role. It does not confirm what is true. It eliminates what cannot be. What remains is a set of possibilities that have survived scrutiny. That is where knowledge begins.

    Hallucinations follow naturally when impossible explanations are never ruled out. The system fills gaps instead of respecting them, and the result is confidence without grounding.

    The models themselves are not the core issue. Imperfection is expected. What matters is how that imperfection is represented. When uncertainty is hidden behind fluent language, the output feels authoritative regardless of its foundation.

    In this keynote, Prof. Moriba Jah introduces the Theory of Epistemic Abductive Geometry, a framework for reasoning under deep uncertainty that centers falsification, comparison, and bounded inference. TEAG allows AI systems to respond with honesty and humility. It creates space for a system to say, “I don’t know,” and to continue with, “Here is what remains standing under the scrutiny of evidence.”

    This approach produces outputs that are traceable, bounded, and auditable. It supports decisions in domains where uncertainty cannot be reduced away, including climate risk, conflict, and space sustainability.

    Comparison is often called the thief of joy. It is also the source of information. Intelligence depends on the ability to distinguish, to eliminate, and to retain what survives.

    The future of AI will be shaped by systems that know the limits of what they can say, and that make those limits visible.

     

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