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Found 2 papers, 2 papers with code
Model Learning with Personalized Interpretability Estimation (ML-PIE)
The latter is estimated by a neural network that is trained concurrently to the evolution using the feedback of the user, which is collected using uncertainty-based active learning.
Learning a Formula of Interpretability to Learn Interpretable Formulas
We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability.
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