  The demand for open and trustworthy AI models points towards widespread
publishing of model weights. Consumers of these model weights must be able to
act accordingly with the information provided. That said, one of the simplest
AI classification models, Logistic Regression (LR), has an unwieldy
interpretation of its model weights, with greater difficulties when extending
LR to generalised additive models. In this work, we show via a User Study that
skilled participants are unable to reliably reproduce the action of small LR
models given the trained parameters. As an antidote to this, we define
Linearised Additive Models (LAMs), an optimal piecewise linear approximation
that augments any trained additive model equipped with a sigmoid link function,
requiring no retraining. We argue that LAMs are more interpretable than
logistic models -- survey participants are shown to solve model reasoning tasks
with LAMs much more accurately than with LR given the same information.
Furthermore, we show that LAMs do not suffer from large performance penalties
in terms of ROC-AUC and calibration with respect to their logistic counterparts
on a broad suite of public financial modelling data.
