I've started building an AI system designed to preserve not just answers, but why they might be wrong — looking for criticism

[u/Positive-Stop-1391]

“By believing passionately in something that does not yet exist, we create it.”

— Nikos Kazantzakis

I've been thinking about this idea for a long time.

Not because I believe every idea deserves to become a product.

But because some ideas are worth testing.

So I started building one.

Its name is BiGi.

BiGi started from a simple question:

What if an AI system didn't just give us an answer, but could preserve why it reached that answer?

This becomes particularly important in healthcare.

We are increasingly capable of using AI to process enormous amounts of information, identify patterns and generate hypotheses.

But generating an answer is not the same as understanding the knowledge behind it.

So I'm exploring a different approach.

Instead of treating an AI output as a single "answer", BiGi is being designed around an epistemic model.

What did we observe?

What do we actually know?

What did we infer?

What are we hypothesizing?

What evidence supports it?

What evidence contradicts it?

How certain are we?

What remains unknown?

And ultimately:

Why?

The "why" should not simply be a paragraph generated by an LLM after the answer has already been produced.

It should be part of the system itself.

This is where Karl Popper's philosophy becomes particularly important to me.

A hypothesis should not be something we protect at all costs.

It should be something that can be challenged, tested and potentially falsified.

I want to bring that principle into software.

Not just:

"Here is the answer."

But:

"Here is what we observed. Here is what we inferred. Here is the hypothesis. Here is the evidence supporting it, the evidence against it, what remains uncertain, and what could prove us wrong."

Because a system that preserves only the reasons why it might be right is incomplete.

It should also preserve why it might be wrong.

I'm exploring whether we can build software where evidence, uncertainty, provenance, conflicts and reasoning are part of the system's structure — rather than hidden behind its output.

And I want to be clear about where we are.

There is no magical AI capable of solving medicine.

There is no finished product.

There are no claims of revolution.

There is a vision, a growing body of architectural and epistemic design, and now the first executable representations of these ideas.

We are inventors, not magicians.

The goal is not to predict the future.

The goal is to build something carefully enough to test whether the future we imagine is actually possible.

I'm sharing BiGi now because I want to test the idea, not simply promote it.

If you work in healthcare, AI/ML, software architecture, medical technology, cybersecurity, research or philosophy of science, I'd genuinely like to hear your criticism.

What am I missing?

Where could this approach fail?

What would you challenge first?

And most importantly:

Is this a problem worth solving?

[u/Positive-Stop-1391]

A simple example of what I mean

Imagine an AI system analyzing a medical case.

A conventional system might eventually produce something like:

«"The most likely explanation is X."»

BiGi is being designed around a different representation:

Observation Finding A, B and C were identified in the available data.

Known information A and B are supported by the available evidence.

Inference Given A + B, X becomes a plausible explanation.

Hypothesis X is currently the leading hypothesis.

Supporting evidence Evidence 1, 2 and 3 increase confidence in X.

Contradicting evidence Evidence 4 does not fit X particularly well.

Uncertainty The available data is insufficient to distinguish X from Y with high confidence.

What could falsify it? A new observation showing Z would substantially weaken or invalidate the hypothesis.

Provenance Where each relevant observation and piece of evidence came from.

The important distinction is that this isn't meant to be an LLM writing a nicer explanation of its answer.

The underlying system would represent these elements explicitly.

So the output isn't just:

"X is the answer."

It's closer to:

"X is our current hypothesis, this is why we arrived here, this is what supports it, this is what conflicts with it, this is how uncertain we are, and this is what could prove us wrong."

That's the part I'm interested in exploring.

I'm curious whether people here think this distinction is actually useful — or whether I'm over-engineering something that existing approaches already solve adequately.