Post hoc analysis can certainly be useful, especially if you’re primarily concerned with the behavior of a specific deployed model. But looking at a static model will not tell you why the model developed a behavior. The real causal story must go back to the training process.
Models are not static objects. They're snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization. But most research treats them as fixed artifacts, analyzing behaviors after training instead of asking why they emerged.
A test for progress: a science of AI should support progressively stronger forms of understanding.
1. Predict outcomes from early training signals
2. Intervene to correct trajectories on undesirable paths
3. Design training procedures that reliably produce desired properties
In film, "we'll fix it in post" is what you say when something went wrong on set and you don't want to redo it. AI research has made it our entire methodology: train the model, then patch whatever comes out. Our new ICML oral argues this can't be the basis of a science of AI. 🧵