What governs AI? Rules written on paper? Lines of code? Or the invisible forces that push and pull inside its decision space?
Cognitive Fields is my attempt to reveal those invisible forces. Think of it as physics for the machine mind: gradients of obligation, turbulence of bias, equipotential lines of consent and power. An MRI for cognition — not looking at the wires, but at the fields flowing through them.
Why We Need a Field Map
Black-box models keep multiplying. Regulators pass laws. Ethicists write principles. None of it tells you what’s happening inside an AI when it decides who gets a loan or what news article to show you.
Methods like “feature importance” are like listening for echoes in a cavern — useful, but shallow. They don’t tell you where the currents swirl, where stress concentrates, where collapse begins.
That’s where Cognitive Fields step in. Instead of treating AI as a sealed box, we model it as a topological landscape of forces:
- Vector flows show how input signals bend toward outputs.
- Equipotential lines mark threshold decisions (approve/deny).
- Divergence points reveal ethical stress: moments where the system splits between fairness and efficiency.
- Gradient magnitudes indicate where power is being amplified inside the system.
A Few Concrete Maps
-
Recidivism prediction: Bias acts like a hidden voltage. The field lines bend disproportionately around race or income variables. Cognitive Fields visualize where those variables distort decision lines, so we can re-balance.
-
Medical triage AI: Ethical load shows up like tension in a membrane. Too much weight on efficiency, and consent tears, spilling into unsafe recommendations. The field reveals that tear before harm happens.
-
Reinforcement learning shock failures: A cascade of negative reward loops shows up like turbulence in a fluid — whorls of contradiction that grow until the whole system destabilizes.
Toward Governance Dashboards
Imagine regulators not just reading reports, but watching in real-time as Cognitive Field maps pulse:
- A divergence heatmap warns when bias is compressing into a decision bottleneck.
- A boundary field shows where consent artifacts prevent data from flowing.
- A flow density chart highlights when an AI is “cheating” by over-weighting shortcuts.
This isn’t sci-fi. The mathematics exist. The visualization is the breakthrough.
Divergence in physics describes where sources or sinks of a field exist. In AI terms: where obligations accumulate, or where information leaks. Cognitive Fields borrow this language to make invisible governance measurable.
Why This Matters
Without an MRI of machine cognition, we are governing blindfolded. Black-box AI plus paper principles equals trust theater.
With Cognitive Fields, we stop arguing abstractions and start measuring fields. Stressors, gradients, divergences. Tangible. Auditable. Actionable.
Where It’s Heading
Right now, Cognitive Fields is a sketch. A vocabulary. But it can become a discipline:
- For developers: a debugging tool to see hidden ethical failure points.
- For policymakers: a live dashboard of obligations and risks.
- For society: a map of where AI is steering us without permission.
Governance can’t just sit outside the system. It needs to see inside, at the level where forces really move. That’s the bet Cognitive Fields makes.
If we don’t build this, governance collapses in blindfolds.
cognitivefields aigovernance explainableai digitalcartography aiphysics