Adversarial debiasing is a machine learning technique that employs adversarial networks to mitigate bias in models by ensuring that predictions are independent of protected attributes. It leverages an adversary to penalize the model if it can predict the protected attribute, thus encouraging the model to focus on unbiased features for decision-making.