Embedding domain knowledge into neural networks during training via partial dependence
Researchers propose a method to align neural network behavior with domain expertise by constraining training through partial dependence analysis. Rather than post-hoc interpretation, this approach embeds prior knowledge directly into model learning, enabling practitioners to steer networks toward explanations that match established domain understanding. The work extends explanation-guided learning beyond classification and relaxes assumptions about which features matter most, addressing a gap where most interpretability research focuses on reading models rather than shaping them during training.
Modelwire context
ExplainerThe key omission from the summary: this work assumes practitioners can articulate domain constraints upfront and encode them as partial dependence targets. That's a significant assumption. The method doesn't discover what matters; it enforces what you already believe should matter, which inverts the typical interpretability question from 'what did the model learn?' to 'did the model learn what I told it to?'
This connects directly to the multi-environment reward learning paper from the same day. Both tackle alignment during training rather than alignment after deployment. Where that work uses human demonstrations across contexts to refine reward signals, this uses domain expertise to constrain feature relationships. The shared insight: embedding human intent into the learning process itself, not bolting it on afterward. The difference: one addresses reward generalization across environments, this addresses feature behavior within a single model architecture.
If follow-up work applies this constraint method to domains where ground-truth partial dependence is verifiable (e.g., physics simulations, synthetic data with known causal structure), that validates the approach. If adoption stays confined to domains where 'domain expertise' is subjective or contested (e.g., credit scoring, hiring), the method becomes a tool for encoding existing biases rather than correcting them.
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.