Let Us Summarize What We Have Discussed So Far
-
AI does not understand the world.
-
What AI handles is nothing more than smooth computation.
-
Real-world judgment contains discontinuities and non-linear breaks.
-
To handle them, logic, ontologies, and DSLs must be externalized.
-
Multi-agent systems are designed on the assumption that agreement does not always exist.
So, finally, we arrive at this question:
Where do humans remain?
What Was Wrong with the Traditional Human-in-the-loop?
Conventional Human-in-the-loop has often been understood in the following way:
-
AI makes the decision
-
Humans perform a final check
-
If there is a problem, they stop it
At first glance, this appears safe.
In reality, this structure rarely functions well.
Because humans inevitably feel this:
“If the AI has already decided this much,
I can no longer take real responsibility.”
As a result:
-
Superficial confirmations
-
Blind endorsement of judgments that are not understood
-
Shifting of responsibility when accidents occur
become common.
This design treats humans as nothing more than brakes.
Redefining Human-in-the-loop as a Role
Here, we must reverse our perspective.
Humans are not:
-
Exception-handling devices
-
Final confirmation buttons
-
Insurance policies for AI
The role humans should play is far more explicit.
Humans are the ones who define discontinuities.
Three Things Only Humans Can Do
Within this design philosophy, the human role is clear.
1. Define boundaries
What should be delegated to computation,
and where logic must intervene.
What is permitted,
what is prohibited,
and where meaning reverses.
Only humans can decide this.
These are value judgments.
They cannot be replaced by computation.
2. Take responsibility
AI does not explain reasons.
Computation merely produces results.
Someone must answer:
-
Why this rule exists
-
Why this constraint takes priority
That subject is human.
That is why judgment must be written outside the system,
in human language—
as contracts, DSLs, and policies.
3. Decide when to update
The world changes.
Rules and boundaries are never permanent.
But:
-
When to change
-
Why now
-
What to discard and what to preserve
These decisions must still be made by humans.
AI can detect change.
It cannot carry resolve.
From Human-in-the-loop to Human-as-Author
At this point, even the term Human-in-the-loop should be replaced.
Humans are not inside the loop.
Humans are the authors of the decision structure.
They write:
-
Ontologies
-
DSLs
-
Priorities
-
Stop conditions
AI executes them faithfully.
The relationship becomes:
-
Humans: write meaning and discontinuities
-
AI: perform smooth computation
-
Multi-agent systems: make judgment conflicts visible
Only then does AI expand human judgment
without replacing it.
Final Conclusion
AI does not think.
It does not understand.
But this is not a flaw.
The real problem is this:
allowing AI to compute where humans should be thinking.
Humans take responsibility for discontinuities.
AI is entrusted with continuity.
When that boundary is made explicit,
AI becomes safe, powerful, and accountable.
To avoid painting the world in uniform gray.
Humans are not the last decision-makers left behind.
They are the ones who write the boundaries first.
This is the redefinition of Human-in-the-loop
in the age of AI.
Related Concrete Designs (Design Notes)
The design described in this article—
fixing discontinuities outside computation and preserving them as decision structures—
is organized as concrete design principles and structures
in the following GitHub repositories.
None of these are presented as final forms.
Their purpose is to leave behind, in a readable form:
-
Where meaning switches
-
Which judgments are fixed as DSLs
-
Where decisions are returned to humans
They are design notes intended to preserve
the decision structure itself for later reading and inspection.
Core Designs
decision-pipeline-reference
A design reference for fixing judgment outside code,
explicitly defining boundaries, responsibilities, and stop conditions
as contracts and DSLs.
ai-decision-system-map
A system-level map that provides a structural overview of
judgment, computation, visualization, and human intervention.
Designs for Handling Discontinuities and Conflicts in Judgment
multi-agent-orchestration-design
An orchestration design that treats multiple judgment agents in parallel,
assuming disagreement,
while remaining readable and auditable.
decision-metric-design
Design principles for fixing judgments
not as optimization outputs,
but as metrics with explicit meaning.
Designs for Returning Judgment to Humans
(Contextualization and Visualization)
ai-decision-visualization
A view-layer design that transforms AI computation results
into forms humans can actually judge.
social-context-inference
An inference design that externalizes relationships and context—
elements that cannot be reduced to numbers—
as part of the decision structure.
コメント