Cartographers of the AI Latent

Here's our innovative idea. Instead of trying to 'open' the AI Black Box we propose to map it.
Imagine an interface that doesn't ask the AI 'why did you choose this?', because the AI often
invents plausible but false answers. Imagine instead a tool that visualizes the topography
of concepts in the AI's latent space.
Our proposal is to build a Cognitive Mapping Interface that extracts the model's internal
representations and visualizes how important concepts are organized in its latent space.
Rather than asking the AI to explain its decisions we analyze the relationships among concepts to identify hidden biases and potentially unsafe reasoning before the model is
deployed in critical applications.
Let's make a concrete example. An AI must decide who gets a transplant. We don't see the
code, but we see the map: is the concept 'elderly patient' geometrically close to 'low priority' or to 'right to care'? If it's close to the first, we've found a structural bias in the geometry of
the machine's thought. This can be entirely implemented by vectorizing the tokens/words
and calculate the cosine similarities among them.
We don't open the box: we read the map of its inner world.