Adversarial Debiasing

Adversarial Debiasing is a technique used to reduce bias in machine learning models. It involves training a model to make accurate predictions while simultaneously preventing it from learning biased or unfair associations that reflect sensitive attributes like race or gender. This is achieved by using an adversarial component: a second model that tries to detect bias based on these attributes. The main model learns to make correct predictions in a way that confuses the bias detector, resulting in fairer outcomes. This approach helps ensure that AI decisions are less influenced by unfair factors, promoting more equitable results.