Creating the Conditions for Trust

Creating the Conditions for Trust

Trust is a funny thing.

We talk about it as though it’s binary.

I trust you.

Or:

I don’t.

But that’s not really how trust works.

Trust accumulates.

Interaction by interaction.

Experience by experience.

A commitment made.

A commitment kept.

A result delivered.

A result reproduced.

An expectation met.

Again.

And again.

Eventually, all of those interactions become something more than individual experiences.

They become a history.

And that history changes how we approach the next interaction.

Confidence is local. Trust is cumulative.

We can develop confidence from a single interaction.

I can examine an analysis, understand how it was performed, scrutinize the evidence, and develop confidence in that particular result.

But trust is different.

Trust asks a broader question:

Can I rely on this?

That usually takes time.

Consistency matters.

Reliability matters.

Dependability matters.

Transparency matters.

Each interaction gives us another piece of evidence.

Eventually, we stop approaching every interaction from zero.

We begin carrying the history forward.

Confidence is local.

Trust is cumulative.

Trust is the ogive

If you survived a statistics class somewhere along the way, you may vaguely remember an ogive.

It’s simply a cumulative curve.

As observations accumulate, the curve grows.

I think trust works something like that.

Early interactions can matter enormously because there isn’t much history yet.

Over time, repeated experience begins to establish what we should expect.

This person is dependable.

This process is reliable.

This system produces results I can rely on.

The history accumulates.

And importantly, the next interaction becomes another part of that history.

The yellow card

I’ve experienced this firsthand while developing an analytical system over the past several months.

Early on, every result deserved scrutiny.

Run the analysis.

Check the result.

Change something.

Run it again.

Compare it with the prior result.

Validate it externally.

Does it reproduce?

Does it make sense?

Again.

And again.

Over time, the system accumulated a history of producing consistent, reproducible results.

Trust began to build.

Then one day, a result was inconsistent.

And immediately…

Yellow card.

Not red.

I didn’t suddenly conclude the entire system was unreliable.

Its history had earned it more than that.

Instead, the reaction was:

That’s unusual. Why?

What changed?

Was the data different?

Was the specification different?

Did something break?

Did I make a mistake?

Can I replicate it?

Someone you’ve worked with successfully for ten years misses a small commitment and you probably respond similarly.

That’s unusual. I wonder what happened.

Someone you’ve known for ten minutes misses the same commitment?

You may draw a very different conclusion.

Same event.

Different history.

Different interpretation.

Earned trust gives us reason to investigate an inconsistency before allowing it to redefine the entire relationship.

But it doesn’t give anyone – or anything – immunity.

The yellow card still came out.

What happens next matters

Trust isn’t built by never being wrong.

People make mistakes.

Processes fail.

Models miss things.

Systems burp.

What happens next matters enormously.

Do we hide the mistake?

Explain it away?

Blame something else?

Or can we understand what happened?

Can we inspect it?

Can we trace the result?

Can we acknowledge the mistake?

Can we correct it?

Sometimes a mistake that is acknowledged, understood and corrected can actually reinforce trust.

Because the interaction teaches us something new:

When something goes wrong, I can trust what happens next.

Open the box

That’s where transparency matters.

But transparency doesn’t mean overwhelming everyone with everything.

Most people don’t need every calculation, every assumption, every intermediate result and every methodological detail before they can make a decision.

They need the appropriate level of information to understand what happened, why it matters, and how much confidence they should place in it.

But if they want to go deeper?

Open the box.

Make all of the evidence available.

The assumptions.

The decisions made along the way.

The methodology.

The limitations.

Make the ingredients and the recipe that went into making the sausage available.

Start with what someone needs to understand.

Then allow them to keep going.

All the way down.

That’s progressive transparency.

Open Box – No Black Box.

Not because everyone will inspect everything.

Most won’t.

But knowing they can matters – it helps build, and reinforce trust.

And when something doesn’t look right, it matters enormously.

Creating the conditions

You can’t demand trust.

You can’t put TRUST on a corporate values poster and make it so.

You can’t tell someone to trust the numbers.

Or the model.

Or the process.

Or the AI.

And “trust me” has never been much of a transparency strategy.

What you can do is create the conditions in which trust becomes reasonable.

Provide evidence.

Create understanding.

Be appropriately transparent.

Allow deeper scrutiny.

Acknowledge uncertainty.

Be consistent.

Be reliable.

Be dependable.

Then do it again.

And again.

Over time, confidence accumulates.

And trust can emerge.

For lack of a conclusion…

Think about something you trust.

A person.

A process.

A metric.

A model.

A system.

Ask yourself:

Why do I trust it?

Chances are, it isn’t because someone once told you that you should.

There’s probably a history.

A collection of interactions that taught you whether you could rely on it.

And there will be another interaction tomorrow.

Because trust is never entirely finished.

Trust is a function of all history – as well as the next interaction.

You don’t create trust by asking someone to trust you.

You create trust by giving them repeated reasons to.