When probabilities are presented, people feel reassured.
Success probability: 82%
Risk: 18%
Confidence: 0.91
When numbers line up like this,
we feel as though the world has become slightly more understandable.
But here lies a major misunderstanding —
one that is especially characteristic of the AI era.
Probability is not a tool for reducing uncertainty.
Why Does Probability Create a Sense of Security?
The reason probability is preferred is simple.
It appears clear.
It allows comparison.
It makes decisions easier.
Probability draws boundary lines in an ambiguous world.
But those boundary lines
were not originally present in reality.
What Probability Really Does: Compression
What is probability?
It is the act of compressing
a collection of uncertain states
into a single numerical value.
Unknown factors.
Unmeasured variables.
Branches of the future.
All of these are folded behind the number.
Probability has not “understood” the world.
It has merely simplified it.
In AI, Probability Easily Becomes a Substitute for Judgment
When AI outputs probabilities,
they are often used like this:
“Choose the option with the higher probability.”
As computation, this is correct.
As judgment, it is fragile.
Because whether the difference between 0.6 and 0.4
actually means something
is determined outside the number itself.
Probability Is Not Meant to Eliminate Hesitation
What people secretly expect from probability is this:
“I don’t have to hesitate anymore.”
But the true role of probability is almost the opposite.
Its value lies in accurately indicating
that we should still hesitate.
When probabilities are split,
when variance is large,
when confidence intervals are wide —
these are signals
that judgment should be suspended.
When Uncertainty Is Erased, Judgment Runs Wild
When probability is treated as “the answer,”
the following happens:
Exceptions become invisible.
Concerns are silently dismissed.
“Unexpected” events multiply.
“The probability is low, so we don’t need to consider it.”
This attitude is the most dangerous of all.
Probability Is a Tool for Preserving Ambiguity
Let us invert the perspective.
Probability is not a number for deciding.
It is a representation that preserves undecidability.
When we think this way,
our design philosophy changes.
What Does It Mean to Design for Preserving Ambiguity?
Instead of eliminating ambiguity,
we keep it in a form that can be handled.
Such design has several key principles:
1. Do Not End with a Single Probability
Output distributions.
Show intervals.
Present multiple scenarios side by side.
Do not collapse everything into one number.
2. Make the Meaning of Probability Explicit
Probability of what?
Under which assumptions?
What is excluded?
Probability is always a hypothesis conditioned on premises.
3. Involve Humans Especially When Probability Is High
This is paradoxical, but essential.
High probability.
High score.
High confidence.
These are precisely the moments to ask:
“Is this truly acceptable?”
Certainty is often the greatest blind spot.
Probability Assists Judgment, But Does Not Replace It
Probability is powerful.
But its power does not lie
in eliminating the need for judgment.
Rather, it:
Makes hesitation visible.
Reminds us of the unknown.
Reveals the weight of decision.
Summary
Probability does not eliminate uncertainty.
Probability compresses the world.
Numbers do not substitute for judgment.
Ambiguity is not something to erase, but something to preserve.
Judgment happens outside probability.
AI can produce probabilities.
But how to take responsibility for them
is something only humans can decide.
For more concrete machine learning approaches,
please refer to the blog posts
“An Overview and Implementation of Probabilistic Optimization in Machine Learning”
and “Probabilistic Approaches in Machine Learning.”
Those who intend to apply these ideas in practice may find them useful.
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