Explainability Is Not the Same Thing as Understanding
Why knowing how a system works does not mean knowing what it means - and why confusing the two is becoming dangerous.
TL;DR (since we’re all allegedly busy)
Explainability tells you how a system produced an output.
Understanding tells you whether that output should matter, in what context, and with what consequences.
We are investing heavily in explainability while quietly dismantling understanding.
That trade is not neutral. It shifts authority, diffuses responsibility, and turns judgment into a procedural artifact.
If you confuse explainability with understanding, you don’t get safer systems.
You get more confident mistakes.
1. The Comfort Lie of Explainability
Explainability feels like progress.
It has the right vibe:
Transparency
Accountability
Ethics
Control
When someone says, “The model is explainable,” what they’re really saying is:
“You can inspect the machinery.”
And that feels reassuring - especially to engineers, regulators, and executives who need something checkable, auditable, and defensible.
But here’s the problem:
You can fully explain a system and still not understand the situation it’s operating in.
Those are different cognitive acts.
2. What Explainability Actually Gives You
Let’s be precise.
Explainability typically provides:
Feature attribution
Model logic summaries
Weight importance
Decision trees or approximations
Saliency maps
In short:
Why the model did what it did.
That’s useful.
Necessary, even.
But it answers a mechanical question, not a meaningful one.
Explainability answers:
“Which inputs mattered?”
“How did this score get produced?”
“Why did the model rank this higher than that?”
It does not answer:
“Is this the right question?”
“Is this the right framing?”
“Is this the right decision to act on?”
“What does this output mean in the real world?”
That gap is where understanding lives.
3. Understanding Is Not a Property of the Model
This is the category error we keep making.
We talk about:
“The model understands”
“The system has understanding”
“The AI knows”
No. It doesn’t.
Understanding is not a system attribute.
It is a human cognitive act.
Understanding requires:
Context
Norms
Purpose
Values
Consequence awareness
Models manipulate representations.
Humans interpret meaning.
When we conflate the two, we stop noticing when meaning drops out of the loop.
4. The Map Is Not the Territory (Still)
This is an old lesson. We just keep forgetting it.
Explainability gives you a better map.
Understanding requires knowing whether you’re even in the right territory.
A perfectly explained model can still:
Optimize the wrong objective
Reinforce the wrong baseline
Normalize harmful patterns
Encode historical bias as “truth”
Explainability does not correct framing errors.
It only clarifies them.
5. Why Explainability Feels Like Understanding
Explainability feels like understanding because it:
Reduces uncertainty
Provides narrative closure
Creates the illusion of mastery
Once you can explain something, your brain relaxes.
It stops probing.
That’s the danger.
Explanation satisfies curiosity.
Understanding sustains judgment.
Those are not the same psychological states.
6. The Proceduralization of Judgment
Here’s what’s quietly happening across AI-enabled organizations:
Judgment is being replaced by procedure.
Instead of asking:
“Do we agree with this output?”
We ask:
“Was the model explainable?”
“Was the process followed?”
“Did it pass validation?”
That’s not judgment.
That’s compliance.
Explainability becomes a checkbox that allows action to proceed without interpretation.
And once judgment is proceduralized, no one actually owns the decision.
7. Why This Matters More in High-Stakes Contexts
In low-stakes environments, misunderstanding is annoying.
In high-stakes environments, it’s lethal.
Think about domains where:
Time pressure is high
Consequences are asymmetric
Errors propagate quickly
Explainability helps you defend the system after the fact.
Understanding helps you intervene before the wrong action becomes irreversible.
We are building systems optimized for post-hoc justification, not pre-action wisdom.
8. Explainability Can Increase Overconfidence
This is deeply counterintuitive, but well documented in practice:
The more explainable a system appears, the more people over-trust it.
Why?
Because explanation creates a sense of shared cognition:
“I see how it thinks, therefore I understand it.”
But seeing how something thinks is not the same as knowing whether it should.
Explainability can make bad frames harder to challenge, because they now look rational.
9. Understanding Requires Friction
Understanding is slow.
It requires:
Competing interpretations
Disagreement
Ambiguity
Narrative reasoning
Moral consideration
Modern AI systems are optimized to remove friction.
Explainability reduces friction further by smoothing uncertainty into diagrams and charts.
Understanding, by contrast, often increases friction.
And friction is now treated as failure.
10. The Disappearance of “Should”
Explainability answers “why did the system do this?”
Understanding asks “should we care?”
Notice how rarely systems prompt the second question.
Dashboards show:
Scores
Rankings
Probabilities
They do not show:
Ethical tension
Contextual misalignment
Strategic ambiguity
The “should” layer is silently dropped.
Not because it’s unimportant - but because it’s hard to formalize.
11. Meaning Is Not Explainable (And That’s the Point)
Meaning cannot be fully explained in advance.
It emerges from:
Situation
Culture
Timing
Intent
History
That makes it incompatible with static explanations.
So we try to replace meaning with metrics.
And then we act surprised when outcomes feel hollow, wrong, or misaligned.
Explainability gives you clarity.
Understanding gives you wisdom.
One scales. The other doesn’t.
That doesn’t make understanding obsolete.
It makes it precious.
12. The False Promise of “More Transparency”
We keep saying:
“If only the systems were more transparent…”
Transparency is not the same as comprehension.
You can expose every line of logic and still fail to grasp:
What the output implies
What it omits
What it normalizes
Transparency without interpretation is just visibility without insight.
13. Understanding Is a Social Process
This is another thing explainability can’t replace.
Understanding emerges through:
Conversation
Argument
Story
Shared context
Explainability is individual and technical.
Understanding is collective and interpretive.
When organizations replace discussion with dashboards, they don’t get alignment.
They get quiet compliance.
14. Why Explainability Became the Focal Point
Explainability won because it fits existing institutions.
Regulators can audit it
Engineers can build it
Lawyers can defend it
Executives can point to it
Understanding doesn’t fit neatly anywhere.
It resists standardization.
So we chose the thing we could measure, not the thing we needed.
15. Understanding Is an Ethical Act
Understanding requires someone to say:
“I see this output - and I take responsibility for interpreting it.”
That’s uncomfortable.
It means:
Owning uncertainty
Owning consequences
Owning moral judgment
Explainability allows responsibility to diffuse into process.
Understanding concentrates responsibility in people.
Guess which one institutions prefer.
16. What We’re Actually Losing
If this trend continues, we don’t lose control overnight.
We lose:
Interpretive skill
Contextual sensitivity
Moral courage
Institutional memory
People become fluent in systems and illiterate in meaning.
That’s not intelligence.
That’s dependency.
17. Re-centering Understanding (Without Romanticizing It)
This is not a call to reject explainability.
Explainability is necessary.
It is just not sufficient.
What we need alongside it:
Explicit interpretive roles
Time carved out for judgment
Systems that surface ambiguity instead of hiding it
Cultural permission to say “this doesn’t make sense yet”
Understanding must be protected as a function, not assumed as a byproduct.
Closing: A Hard Line Worth Holding
Explainability tells you how a decision happened.
Understanding tells you whether it should happen at all.
Confusing the two is how authority erodes without anyone noticing.
We don’t need systems that merely explain themselves.
We need systems that leave room for humans to understand.
Because once understanding disappears, explanation just becomes the story we tell ourselves about decisions no one really made.