  We are interested in learning data-driven representations that can generalize
well, even when trained on inherently biased data. In particular, we face the
case where some attributes (bias) of the data, if learned by the model, can
severely compromise its generalization properties. We tackle this problem
through the lens of information theory, leveraging recent findings for a
differentiable estimation of mutual information. We propose a novel end-to-end
optimization strategy, which simultaneously estimates and minimizes the mutual
information between the learned representation and the data attributes. When
applied on standard benchmarks, our model shows comparable or superior
classification performance with respect to state-of-the-art approaches.
Moreover, our method is general enough to be applicable to the problem of
``algorithmic fairness'', with competitive results.
