Co-creating a globally interpretable model with human input
Rahul Nair
We consider an aggregated human-AI collaboration aimed at generating a joint
interpretable model. The model takes the form of Boolean decision rules, where
human input is provided in the form of logical conditions or as partial
templates. This focus on the combined construction of a model offers a
different perspective on joint decision making. Previous efforts have typically
focused on aggregating outcomes rather than decisions logic. We demonstrate the
proposed approach through two examples and highlight the usefulness and
challenges of the approach.