I am very excited to announce our next paper on Discrimination Free Insurance Pricing (DFIP). The first paper introduced a method for removing indirect discrimination from pricing models. The DFIP technique requires that the discriminatory features (e.g. gender) are known for all examples in the data on which the model is trained, as well as for the subsequent policies which will be priced. In this new work, we only require that the discriminatory features are known for a subset of the examples and use a specially designed neural network (with multiple outputs) to take care of those examples that are missing this information. In the plot below, we show that this new approach produces excellent approximations to the true discriminatory free price in a synthetic health insurance example.
The new paper can be found here:
Thank you to Mathias Lindholm, Andreas Tsanakas and Mario Wüthrich for this wonderful collaboration!