CNM for AI is at the Intersection
Exposing the Logic Behind AI Decision-Making
The network analytics software developed in systems biology to find causal mechanisms of disease is being adapted to AI to uncover the underlying logic behind model decision-making.
Standard evals can measure whether outputs improve or degrade, but they do not reveal whether the model’s internal decision structure has changed. By looking beneath the final output, CNM helps reveal whether the model is relying on stable, meaningful logic or on hidden patterns that may shift after the model is changed.
CNM Maps the Causal Backbone of AI Decision-Making
Using subnetwork functional enrichment, CNM identifies a causal backbone that can be compared across model versions as a model is changed for latency, cost, domain adaptation, RLHF, safety, or performance. This allows teams to see whether an update preserves the model’s underlying decision logic or quietly changes how the model reaches its answers. CNM helps reveal hidden drift in the reasoning structure before it appears as downstream failure.
Establishing Trust
When the logic behind AI decisions can be understood and trusted, AI can be used more safely in high-stakes, regulated, and complex fields such as medicine, finance, and scientific discovery. CNM helps reveal whether a model’s answers are supported by stable, meaningful decision structures rather than hidden shortcuts or fragile patterns. By making the underlying logic more visible, CNM creates a foundation for deploying AI where trust, accountability, and reliability matter most.