Debiasing surgeon: fantastic weights and how to find them
Rémi Nahon Ivan Luiz De Moura Matos Van-Tam Nguyen Enzo Tartaglione
Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic
biases that can lead to unfair models, emerges. Several debiasing approaches
have been proposed in the realm of deep learning, employing more or less
sophisticated approaches to discourage these models from massively employing
these biases. However, a question emerges: is this extra complexity really
necessary? Is a vanilla-trained model already embodying some ``unbiased
sub-networks'' that can be used in isolation and propose a solution without
relying on the algorithmic biases? In this work, we show that such a
sub-network typically exists, and can be extracted from a vanilla-trained model
without requiring additional training. We further validate that such specific
architecture is incapable of learning a specific bias, suggesting that there
are possible architectural countermeasures to the problem of biases in deep
neural networks.